A B C D E F G H I K L M N O P R S T U V W

A

activate(double) - Method in class mulan.classifier.neural.model.ActivationFunction

Computes an output value of the function for given input.

activate(double) - Method in class mulan.classifier.neural.model.ActivationLinear

activate(double) - Method in class mulan.classifier.neural.model.ActivationTANH

ActivationFunction - Class in mulan.classifier.neural.model

Abstract base class for activation functions.

ActivationFunction() - Constructor for class mulan.classifier.neural.model.ActivationFunction

ActivationLinear - Class in mulan.classifier.neural.model

Implements the linear activation function.

ActivationLinear() - Constructor for class mulan.classifier.neural.model.ActivationLinear

ActivationTANH - Class in mulan.classifier.neural.model

Implements the hyperbolic tangent activation function.

ActivationTANH() - Constructor for class mulan.classifier.neural.model.ActivationTANH

AdaBoostMH - Class in mulan.classifier.transformation

Implementation of the AdaBoost.MH algorithm based on Weka's AdaBoostM1.

AdaBoostMH() - Constructor for class mulan.classifier.transformation.AdaBoostMH

Default constructor

addAllNeurons(Collection<Neuron>) - Method in class mulan.classifier.neural.model.Neuron

Adds connections to all specified Neuron instances.

addChild(HMCNode) - Method in class mulan.classifier.meta.HMCNode

Adds a child to the node.

addChildNode(LabelNode) - Method in class mulan.data.LabelNodeImpl

Adds the specified LabelNode to the set of child nodes.

addEvaluation(Evaluation) - Method in class mulan.evaluation.MultipleEvaluation

Adds an evaluation results to the list of evaluations

addNeuron(Neuron) - Method in class mulan.classifier.neural.model.Neuron

Adds a connection to a specified Neuron.

addRootNode(LabelNode) - Method in class mulan.data.LabelsMetaDataImpl

Adds a root LabelNode.

all_Predictions - Variable in class mulan.evaluation.measure.LabelBasedAUC

The predictions for all labels

ArgumentNullException - Exception in mulan.core

This exception is raised when a null reference is passed to a method that does not accept a null reference for an argument.

ArgumentNullException(String) - Constructor for exception mulan.core.ArgumentNullException

Creates a new instance of ArgumentNullException for specified parameter.

ArgumentNullException(String, String) - Constructor for exception mulan.core.ArgumentNullException

Creates a new instance of ArgumentNullException with detailed message for specified parameter.

attachIndexes(Instances) - Method in class mulan.classifier.transformation.MultiLabelStacking

Attaches an index attribute at the beginning of each instance

average(double[][], int) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

average score combination approach

AveragePrecision - Class in mulan.evaluation.measure

Implementation of the average precision measure.

AveragePrecision() - Constructor for class mulan.evaluation.measure.AveragePrecision

B

BagSizePercent - Variable in class mulan.classifier.transformation.EnsembleOfClassifierChains

The size of each bag sample, as a percentage of the training size.

baseClassifier - Variable in class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

The underlying single-label classifier.

baseLearner - Variable in class mulan.classifier.meta.MultiLabelMetaLearner

The encapsulated classifier or used for making clones in the case of ensemble classifiers.

baseSingleLabelClassifier - Variable in class mulan.classifier.meta.SubsetLearner

Base single-label classifier that will be used for training and predictions

BasicNeuralNet - Class in mulan.classifier.neural.model

Implementation of basic neural network.

BasicNeuralNet(int[], double, Class<? extends ActivationFunction>, Random) - Constructor for class mulan.classifier.neural.model.BasicNeuralNet

Creates a new BasicNeuralNet instance.

beta - Variable in class mulan.evaluation.measure.LabelBasedFMeasure

the parameter for combining precision and recall

BinaryRelevance - Class in mulan.classifier.transformation

BinaryRelevance(Classifier) - Constructor for class mulan.classifier.transformation.BinaryRelevance

Creates a new instance

BinaryRelevanceAttributeEvaluator - Class in mulan.dimensionalityReduction

BinaryRelevanceAttributeEvaluator(ASEvaluation, MultiLabelInstances, String, String, String) - Constructor for class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

BinaryRelevanceAttributeEvaluator.Rank - Class in mulan.dimensionalityReduction

a wrapper class for score-based attribute ranking

BinaryRelevanceAttributeEvaluator.Rank(double, int) - Constructor for class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator.Rank

constructor

BinaryRelevanceTransformation - Class in mulan.transformations

Class that implements the binary relevance transformation

BinaryRelevanceTransformation(MultiLabelInstances) - Constructor for class mulan.transformations.BinaryRelevanceTransformation

Constructor

binomial(int, int) - Static method in class mulan.classifier.meta.RAkEL

The binomial function

BipartitionLossFunction - Interface in mulan.evaluation.loss

Interfance for bipartition loss functions

BipartitionLossFunctionBase - Class in mulan.evaluation.loss

Base class for bipartition loss functions

BipartitionLossFunctionBase() - Constructor for class mulan.evaluation.loss.BipartitionLossFunctionBase

BipartitionMeasureBase - Class in mulan.evaluation.measure

BipartitionMeasureBase() - Constructor for class mulan.evaluation.measure.BipartitionMeasureBase

BPMLL - Class in mulan.classifier.neural

The implementation of Back-Propagation Multi-Label Learning (BPMLL) learner.

BPMLL() - Constructor for class mulan.classifier.neural.BPMLL

Creates a new instance of BPMLL learner.

BPMLL(long) - Constructor for class mulan.classifier.neural.BPMLL

Creates a new instance of BPMLL learner.

BPMLLAlgorithm - Class in mulan.classifier.neural

The implementation of Back-Propagation Multi-Label Learning (BPMLL) algorithm for neural networks.

BPMLLAlgorithm(NeuralNet, double) - Constructor for class mulan.classifier.neural.BPMLLAlgorithm

Creates a BPMLLAlgorithm instance.

BRkNN - Class in mulan.classifier.lazy

Simple BR implementation of the KNN algorithm.For more information, see

Eleftherios Spyromitros, Grigorios Tsoumakas, Ioannis Vlahavas: An Empirical Study of Lazy Multilabel Classification Algorithms.

BRkNN() - Constructor for class mulan.classifier.lazy.BRkNN

Default constructor

BRkNN(int) - Constructor for class mulan.classifier.lazy.BRkNN

A constructor that sets the number of neighbors

BRkNN(int, BRkNN.ExtensionType) - Constructor for class mulan.classifier.lazy.BRkNN

Constructor giving the option to select an extension of the base version

BRkNN.ExtensionType - Enum in mulan.classifier.lazy

The two types of extensions

build(MultiLabelInstances) - Method in interface mulan.classifier.MultiLabelLearner

Builds the learner model from specified MultiLabelInstances data.

build(MultiLabelInstances) - Method in class mulan.classifier.MultiLabelLearnerBase

build(double[][], double[][]) - Method in class mulan.classifier.neural.ThresholdFunction

Build a threshold function for based on input data.

buildBaseLevel(MultiLabelInstances) - Method in class mulan.classifier.transformation.MultiLabelStacking

Builds the base-level classifiers.

buildBaseLevelKNN(MultiLabelInstances) - Method in class mulan.classifier.transformation.MultiLabelStacking

Used only in case of a kNN base classifier.

buildClusterer(Instances) - Method in class mulan.classifier.meta.ConstrainedKMeans

Generates a clusterer.

buildEvaluator(Instances) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

Not supported

buildEvaluator(Instances) - Method in class mulan.dimensionalityReduction.LabelPowersetAttributeEvaluator

Not supported

buildEvaluator(Instances) - Method in class mulan.dimensionalityReduction.MultiClassAttributeEvaluator

Not supported

buildHierarchy(MultiLabelInstances) - Method in class mulan.classifier.meta.HierarchyBuilder

Builds a hierarhical multi-label dataset.

buildHierarchyAndSaveFiles(MultiLabelInstances, String, String) - Method in class mulan.classifier.meta.HierarchyBuilder

Builds the hierarchy and constructs auxiliary files

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.lazy.BRkNN

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.lazy.IBLR_ML

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.lazy.MLkNN

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.lazy.MultiLabelKNN

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.ClusteringBased

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

Builds an ensemble of Subset models.

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.HMC

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.HMCNode

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.HOMER

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.RAkEL

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.RAkELd

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.SubsetLearner

We get the initial dataset through trainingSet.

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.ExampleBasedFMeasureOptimizer

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.Meta

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.MLPTO

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.OneThreshold

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.RCut

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.SCut

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.MultiLabelLearnerBase

Learner specific implementation of building the model from MultiLabelInstances training data set.

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.neural.BPMLL

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.neural.MMPLearner

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.BinaryRelevance

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.CalibratedLabelRanking

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.ClassifierChain

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.EnsembleOfPrunedSets

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.IncludeLabelsClassifier

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.LabelPowerset

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.LabelsetPruning

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.MultiClassLearner

buildInternal(MultiLabelInstances) - Method in class mulan.classifier.transformation.MultiLabelStacking

buildLabelHierarchy(MultiLabelInstances) - Method in class mulan.classifier.meta.HierarchyBuilder

Builds a hierarhy of labels on top of the labels of a flat multi-label dataset, by recursively partitioning the labels into a specified number of partitions.

buildMetaLevel() - Method in class mulan.classifier.transformation.MultiLabelStacking

Builds the ensemble of meta-level classifiers.

C

calculateCoocurrence(MultiLabelInstances) - Method in class mulan.data.Statistics

This method calculates and prints a matrix with the coocurrences of
pairs of labels

calculateDependence(MultiLabelInstances) - Method in class mulan.data.ConditionalDependenceIdentifier

Calculates t-statistic value for each pair of labels.

calculateDependence(MultiLabelInstances) - Method in interface mulan.data.LabelPairsDependenceIdentifier

Calculates dependence level between each pair of labels in the given multilabel data set

calculateDependence(MultiLabelInstances) - Method in class mulan.data.UnconditionalChiSquareIdentifier

Calculates Chi Square values for each pair of labels.

calculatePhi(MultiLabelInstances) - Method in class mulan.data.Statistics

Calculates phi correlation

calculateStatistics() - Method in class mulan.evaluation.MultipleEvaluation

Computes mean and standard deviation of all evaluation measures

calculateStats(MultiLabelInstances) - Method in class mulan.data.Statistics

calculates various multilabel statistics, such as label cardinality,
label density and the set of distinct labels along with their frequency

CalibratedLabelRanking - Class in mulan.classifier.transformation

Class implementing the Calibrated Label Ranking (CLR) algorithm.

CalibratedLabelRanking() - Constructor for class mulan.classifier.transformation.CalibratedLabelRanking

Default constructor using J48 as underlying classifier

CalibratedLabelRanking(Classifier) - Constructor for class mulan.classifier.transformation.CalibratedLabelRanking

Constructor that initializes the learner with a base algorithm

cardinality() - Method in class mulan.data.Statistics

returns the label cardinality of the dataset

classifier - Variable in class mulan.classifier.meta.thresholding.Meta

the classifier to learn the number of top labels or the threshold

ClassifierChain - Class in mulan.classifier.transformation

Class implementing the Classifier Chain (CC) algorithm.

ClassifierChain() - Constructor for class mulan.classifier.transformation.ClassifierChain

Creates a new instance using J48 as the underlying classifier

ClassifierChain(Classifier, int[]) - Constructor for class mulan.classifier.transformation.ClassifierChain

Creates a new instance

ClassifierChain(Classifier) - Constructor for class mulan.classifier.transformation.ClassifierChain

Creates a new instance

classifierInstances - Variable in class mulan.classifier.meta.thresholding.Meta

the training instances for the single-label model

clone() - Method in interface mulan.data.LabelsMetaData

Returns a deep copy of the LabelsMetaDataImpl instance.

clone() - Method in class mulan.data.LabelsMetaDataImpl

clone() - Method in class mulan.data.MultiLabelInstances

Returns a deep copy of the MultiLabelInstances instance.

ClusteringBased - Class in mulan.classifier.meta

Class implementing clustering-based multi-label classification.

ClusteringBased() - Constructor for class mulan.classifier.meta.ClusteringBased

Default constructor.

ClusteringBased(Clusterer, MultiLabelLearner) - Constructor for class mulan.classifier.meta.ClusteringBased

Constructor

clusterInstance(Instance) - Method in class mulan.classifier.meta.ConstrainedKMeans

Classifies a given instance.

compareTo(Object) - Method in class mulan.classifier.meta.ConstrainedKMeans.bucketInstance

compareTo(LabelSet) - Method in class mulan.data.LabelSet

Used for sorting collections of labelsets according to size

compareTo(Object) - Method in class mulan.data.LabelsPair

compareTo(Object) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator.Rank

compareTo(Object) - Method in class mulan.evaluation.measure.LabelBasedAveragePrecision.ConfidenceActual

computeLoss(boolean[], boolean[]) - Method in interface mulan.evaluation.loss.BipartitionLossFunction

Computes the bipartition loss function

computeLoss(MultiLabelOutput, boolean[]) - Method in class mulan.evaluation.loss.BipartitionLossFunctionBase

computeLoss(boolean[], boolean[]) - Method in class mulan.evaluation.loss.BipartitionLossFunctionBase

computeLoss(int[], boolean[]) - Method in class mulan.evaluation.loss.ErrorSetSize

computeLoss(boolean[], boolean[]) - Method in class mulan.evaluation.loss.HammingLoss

computeLoss(boolean[], boolean[]) - Method in class mulan.evaluation.loss.HierarchicalLoss

computeLoss(int[], boolean[]) - Method in class mulan.evaluation.loss.IsError

computeLoss(MultiLabelOutput, boolean[]) - Method in interface mulan.evaluation.loss.MultiLabelLossFunction

Computes the loss function

computeLoss(int[], boolean[]) - Method in class mulan.evaluation.loss.OneError

computeLoss(int[], boolean[]) - Method in class mulan.evaluation.loss.OneMinusAveragePrecision

computeLoss(int[], boolean[]) - Method in class mulan.evaluation.loss.RankingLoss

computeLoss(int[], boolean[]) - Method in interface mulan.evaluation.loss.RankingLossFunction

Computes the ranking loss function

computeLoss(MultiLabelOutput, boolean[]) - Method in class mulan.evaluation.loss.RankingLossFunctionBase

computeLoss(int[], boolean[]) - Method in class mulan.evaluation.loss.RankingLossFunctionBase

computeThreshold(double[]) - Method in class mulan.classifier.neural.ThresholdFunction

Computes a threshold value, based on learned parameters, for given labels confidences.

computeUpdateParameters(DataPair, double[], double) - Method in class mulan.classifier.neural.MMPMaxUpdateRule

computeUpdateParameters(DataPair, double[], double) - Method in class mulan.classifier.neural.MMPRandomizedUpdateRule

computeUpdateParameters(DataPair, double[], double) - Method in class mulan.classifier.neural.MMPUniformUpdateRule

computeUpdateParameters(DataPair, double[], double) - Method in class mulan.classifier.neural.MMPUpdateRuleBase

Computes update parameters for each perceptron which will be subsequently used for updating the weights.

ConditionalDependenceIdentifier - Class in mulan.data

A class for identification of conditional dependence between each pair of labels.

ConditionalDependenceIdentifier(Classifier) - Constructor for class mulan.data.ConditionalDependenceIdentifier

Initializes a single-label classifier used to perform dependence test between labels and a caching mechanism for reusing constructed models.

confact - Variable in class mulan.evaluation.measure.LabelBasedAveragePrecision

collection that stores all predictions and ground truths

ConfidenceMeasureBase - Class in mulan.evaluation.measure

ConfidenceMeasureBase() - Constructor for class mulan.evaluation.measure.ConfidenceMeasureBase

ConstrainedKMeans - Class in mulan.classifier.meta

Cluster data using the constrained k means algorithm

ConstrainedKMeans() - Constructor for class mulan.classifier.meta.ConstrainedKMeans

the default constructor

ConstrainedKMeans.bucketInstance - Class in mulan.classifier.meta

Class for representing an instance inside a bucket

ConstrainedKMeans.bucketInstance() - Constructor for class mulan.classifier.meta.ConstrainedKMeans.bucketInstance

containsLabel(String) - Method in interface mulan.data.LabelsMetaData

Determines if LabelsMetaData contains a label with specified name.

containsLabel(String) - Method in class mulan.data.LabelsMetaDataImpl

convert(String, String, String) - Static method in class mulan.data.ConverterCLUS

Converts the original dataset to mulan compatible dataset.

ConverterCLUS - Class in mulan.data

Class that converts a dataset that is originally in the format of the Clus system to a format that is suitable for Mulan.

ConverterCLUS() - Constructor for class mulan.data.ConverterCLUS

ConverterLibSVM - Class in mulan.data

Class that converts LibSVM multi-label data sets to Mulan compatible format

ConverterLibSVM() - Constructor for class mulan.data.ConverterLibSVM

convertFromLibSVM(String, String, String, String) - Static method in class mulan.data.ConverterLibSVM

Converts a multi-label dataset from LibSVM format to the format that is compatible with Mulan.

Copy - Class in mulan.transformations.multiclass

Class that implement the Copy transformation method

Copy() - Constructor for class mulan.transformations.multiclass.Copy

CopyWeight - Class in mulan.transformations.multiclass

Class that implement the Copy-Weight transformation method

CopyWeight() - Constructor for class mulan.transformations.multiclass.CopyWeight

count - Variable in class mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase

The number of validation examples processed

count - Variable in class mulan.evaluation.measure.RankingMeasureBase

The number of validation examples processed

Coverage - Class in mulan.evaluation.measure

Implementation of the coverage measure.

Coverage() - Constructor for class mulan.evaluation.measure.Coverage

createDataPairs(MultiLabelInstances, boolean) - Static method in class mulan.classifier.neural.DataPair

Creates a DataPair representation for each Instance contained in MultiLabelInstances data set.

createHierarchicalDataset(MultiLabelInstances, LabelsMetaData) - Static method in class mulan.classifier.meta.HierarchyBuilder

Creates the hierarchical dataset according to the original multilabel instances object and the constructed label hierarchy

createInstance(Instance, double, double[]) - Static method in class mulan.data.DataUtils

Creates a new Instance.

createInstance(Instance, int) - Static method in class mulan.data.DataUtils

Creates a new Instance.

createLabels(String) - Static method in class mulan.data.LabelsBuilder

Creates a LabelsMetaData instance from XML file specified by the path.

createLabels(InputStream) - Static method in class mulan.data.LabelsBuilder

Creates a LabelsMetaData instance from specified input stream.

createRandomSets(int, int) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

Creates the specified number of randomly generated possible label set partitions consisting of the specified number of labels..

crossValidate(MultiLabelLearner, MultiLabelInstances, int) - Method in class mulan.evaluation.Evaluator

Evaluates a MultiLabelLearner via cross-validation on given data set with defined number of folds and seed.

crossValidate(MultiLabelLearner, MultiLabelInstances, List<Measure>, int) - Method in class mulan.evaluation.Evaluator

Evaluates a MultiLabelLearner via cross-validation on given data set using given evaluation measures with defined number of folds and seed.

D

DataLoadException - Exception in mulan.data

The exception is thrown to indicate an error while loading the data.

DataLoadException(String) - Constructor for exception mulan.data.DataLoadException

Creates a new instance of DataLoadException with detail mesage.

DataLoadException(String, Throwable) - Constructor for exception mulan.data.DataLoadException

Creates a new instance of DataLoadException with detail message and nested exception.

DataPair - Class in mulan.classifier.neural

Class for representation of a data-pair instance.

DataPair(double[], double[]) - Constructor for class mulan.classifier.neural.DataPair

Creates a DataPair instance.

DataUtils - Class in mulan.data

Utility class for data related manipulation functions.

DataUtils() - Constructor for class mulan.data.DataUtils

debug(String) - Method in class mulan.classifier.MultiLabelLearnerBase

Writes the debug message string to the console output if debug for the learner is enabled.

deleteInstances(Instances, int) - Method in class mulan.classifier.meta.HMC

Deletes the unnecessary instances, the instances that have value 0 on given attribute.

deleteLabels(MultiLabelInstances, String, boolean) - Method in class mulan.classifier.meta.HMC

Deletes the unnecessary attributes.

density() - Method in class mulan.data.Statistics

returns the label density of the dataset

derivative(double) - Method in class mulan.classifier.neural.model.ActivationFunction

Computes an output value of function derivation for given input.

derivative(double) - Method in class mulan.classifier.neural.model.ActivationLinear

derivative(double) - Method in class mulan.classifier.neural.model.ActivationTANH

detachIndexes(Instances) - Method in class mulan.classifier.transformation.MultiLabelStacking

Detaches the index attribute from the beginning of each instance

determineClusters(MultiLabelInstances) - Method in class mulan.data.GreedyLabelClustering

Determines labels partitioning into dependent sets.

determineClusters(MultiLabelInstances) - Method in interface mulan.data.LabelClustering

Returns a label set partitioning into clusters

dfunc - Variable in class mulan.classifier.lazy.MultiLabelKNN

Implementing Euclidean distance (or similarity) function.

distanceWeighting - Variable in class mulan.classifier.lazy.MultiLabelKNN

Whether the neighbors should be distance-weighted.

dl(double[]) - Static method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

divide by length (dl) normalization

dm(double[]) - Static method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

divide by maximum (dm) normalization

dumpLabels(LabelsMetaData, String) - Static method in class mulan.data.LabelsBuilder

Dumps specified labels meta-data into the file in XML format.

dumpLabels(LabelsMetaData, OutputStream) - Static method in class mulan.data.LabelsBuilder

Dumps specified labels meta-data, in XML format, into the specified OutputStream.

E

ensemble - Variable in class mulan.classifier.transformation.BinaryRelevance

The ensemble of binary relevance models.

ensemble - Variable in class mulan.classifier.transformation.ClassifierChain

The ensemble of binary relevance models.

ensemble - Variable in class mulan.classifier.transformation.EnsembleOfClassifierChains

An array of ClassifierChain models

ensemble - Variable in class mulan.classifier.transformation.EnsembleOfPrunedSets

The models in the ensemble

EnsembleOfClassifierChains - Class in mulan.classifier.transformation

Class implementing the Ensemble of Classifier Chains(ECC) algorithm.

EnsembleOfClassifierChains() - Constructor for class mulan.classifier.transformation.EnsembleOfClassifierChains

Default constructor

EnsembleOfClassifierChains(Classifier, int, boolean, boolean) - Constructor for class mulan.classifier.transformation.EnsembleOfClassifierChains

Creates a new object

EnsembleOfPrunedSets - Class in mulan.classifier.transformation

Class implementing the Ensemble of Pruned Sets algorithm(EPS) .

EnsembleOfPrunedSets() - Constructor for class mulan.classifier.transformation.EnsembleOfPrunedSets

Creates a new instance with default values

EnsembleOfPrunedSets(double, int, double, int, PrunedSets.Strategy, int, Classifier) - Constructor for class mulan.classifier.transformation.EnsembleOfPrunedSets

EnsembleOfSubsetLearners - Class in mulan.classifier.meta

A class for gathering several different SubsetLearners into a composite ensemble model.

EnsembleOfSubsetLearners() - Constructor for class mulan.classifier.meta.EnsembleOfSubsetLearners

Default constructor.

EnsembleOfSubsetLearners(MultiLabelLearner, Classifier, LabelPairsDependenceIdentifier, int) - Constructor for class mulan.classifier.meta.EnsembleOfSubsetLearners

Initialize EnsembleOfSubset with multilabel and single label learners, a method for labels dependence identification and number of models to constitute the ensemble.

equals(Object) - Method in class mulan.classifier.MultiLabelOutput

Tests if two MultiLabelOutput objects are equal

equals(Object) - Method in class mulan.data.LabelNodeImpl

The two LabelNodeImpl nodes are equal if the are the same (points to the same object) of if they returns same LabelNodeImpl.getName() value.

equals(Object) - Method in class mulan.data.LabelSet

equals(Object) - Method in class mulan.data.LabelsPair

ErrorSetSize - Class in mulan.evaluation.loss

Implementation of the ErrorSetSize loss function, which computes the size of the error set.

ErrorSetSize() - Constructor for class mulan.evaluation.loss.ErrorSetSize

ErrorSetSize - Class in mulan.evaluation.measure

Measure based on the ErrorSetSize ranking loss function

ErrorSetSize() - Constructor for class mulan.evaluation.measure.ErrorSetSize

Creates an instance of this object based on the corresponding loss function

evaluate(MultiLabelLearner, MultiLabelInstances, List<Measure>) - Method in class mulan.evaluation.Evaluator

Evaluates a MultiLabelLearner on given test data set using specified evaluation measures

evaluate(MultiLabelLearner, MultiLabelInstances) - Method in class mulan.evaluation.Evaluator

Evaluates a MultiLabelLearner on given test data set.

evaluateAttribute(int) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

Evaluates an attribute

evaluateAttribute(int) - Method in class mulan.dimensionalityReduction.LabelPowersetAttributeEvaluator

Evaluates an attribute

evaluateAttribute(int) - Method in class mulan.dimensionalityReduction.MultiClassAttributeEvaluator

Evaluates an attribute

Evaluation - Class in mulan.evaluation

Simple class that includes a list of evaluation measures returned from a call to the static methods of Evaluator for evaluation purposes.

Evaluation(List<Measure>, MultiLabelInstances) - Constructor for class mulan.evaluation.Evaluation

Creates a new evaluation object by deep copying the measure objects that are given as parameters

Evaluator - Class in mulan.evaluation

Evaluator - responsible for generating evaluation data

Evaluator() - Constructor for class mulan.evaluation.Evaluator

ExampleBasedAccuracy - Class in mulan.evaluation.measure

Implementation of the example-based accuracy measure.

ExampleBasedAccuracy() - Constructor for class mulan.evaluation.measure.ExampleBasedAccuracy

Constructs a new object

ExampleBasedAccuracy(double) - Constructor for class mulan.evaluation.measure.ExampleBasedAccuracy

Constructs a new object

ExampleBasedBipartitionMeasureBase - Class in mulan.evaluation.measure

Base class for example-based bipartition measures

ExampleBasedBipartitionMeasureBase() - Constructor for class mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase

ExampleBasedFMeasure - Class in mulan.evaluation.measure

Implementation of the example-based F measure.

ExampleBasedFMeasure() - Constructor for class mulan.evaluation.measure.ExampleBasedFMeasure

Creates a new object

ExampleBasedFMeasure(double) - Constructor for class mulan.evaluation.measure.ExampleBasedFMeasure

Creates a new object

ExampleBasedFMeasureOptimizer - Class in mulan.classifier.meta.thresholding

This class takes the marginal probabilities estimated for each label by a multi-label learner and transforms them into a bipartition which is approximately optimal for example-based FMeasure.

ExampleBasedFMeasureOptimizer(MultiLabelLearner) - Constructor for class mulan.classifier.meta.thresholding.ExampleBasedFMeasureOptimizer

The supplied multi-label learner should be able to output marginal probabilities.

ExampleBasedFMeasureOptimizer() - Constructor for class mulan.classifier.meta.thresholding.ExampleBasedFMeasureOptimizer

Default constructor

ExampleBasedPrecision - Class in mulan.evaluation.measure

Implementation of the example-based precision measure.

ExampleBasedPrecision() - Constructor for class mulan.evaluation.measure.ExampleBasedPrecision

ExampleBasedRecall - Class in mulan.evaluation.measure

Implementation of the example-based recall measure.

ExampleBasedRecall() - Constructor for class mulan.evaluation.measure.ExampleBasedRecall

ExampleBasedSpecificity - Class in mulan.evaluation.measure

Implementation of the example-based recall measure.

ExampleBasedSpecificity() - Constructor for class mulan.evaluation.measure.ExampleBasedSpecificity

Experiment - Class in mulan.experiments

Abstract base class for all experiments

Experiment() - Constructor for class mulan.experiments.Experiment

F

falseNegatives - Variable in class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

the number of false negative for each label

falsePositives - Variable in class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

the number of false positives for each label

featureIndices - Variable in class mulan.classifier.MultiLabelLearnerBase

An array containing the indexes of the feature attributes within the Instances object of the training data in increasing order.

feedForward(double[]) - Method in class mulan.classifier.neural.model.BasicNeuralNet

feedForward(double[]) - Method in interface mulan.classifier.neural.model.NeuralNet

Propagates the input pattern through the network.

fMeasure(double, double, double, double) - Static method in class mulan.evaluation.measure.InformationRetrievalMeasures

Computation of F-measure based on tp, fp, fn and beta.

foldLearner - Variable in class mulan.classifier.meta.thresholding.Meta

clean multi-label learner for cross-validation

fromBitString(String) - Static method in class mulan.data.LabelSet

Constructs a LabelSet object from a bitstring.

G

GeometricMeanAverageInterpolatedPrecision - Class in mulan.evaluation.measure

Implementation of GMAiP (Geometric Mean Average Interpolated Precision)

GeometricMeanAverageInterpolatedPrecision(int, int) - Constructor for class mulan.evaluation.measure.GeometricMeanAverageInterpolatedPrecision

Creates a new object

GeometricMeanAveragePrecision - Class in mulan.evaluation.measure

Implementation of GMAP (Geometric Mean Average Precision)

GeometricMeanAveragePrecision(int) - Constructor for class mulan.evaluation.measure.GeometricMeanAveragePrecision

Creates a new instance of this class

getActivationFunction() - Method in class mulan.classifier.neural.model.Neuron

Returns the ActivationFunction used by the Neuron.

getActual() - Method in class mulan.evaluation.measure.LabelBasedAveragePrecision.ConfidenceActual

Returns the ground truth

getAllowedNonImprovementSteps() - Method in class mulan.data.GreedyLabelClustering

getBagSizePercent() - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

Returns the size of each bag sample, as a percentage of the training size

getBaseClassifier() - Method in class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

Returns the Classifier which is used internally by the learner.

getBaseLearner() - Method in class mulan.classifier.meta.MultiLabelMetaLearner

Returns the MultiLabelLearner which is used internally by the learner.

getBiasInput() - Method in class mulan.classifier.neural.model.Neuron

Returns a bias input value.

getBipartition() - Method in class mulan.classifier.MultiLabelOutput

Gets bipartition of labels.

getCapabilities() - Method in class mulan.classifier.meta.ConstrainedKMeans

Returns default capabilities of the clusterer.

getCardinality() - Method in class mulan.data.MultiLabelInstances

Gets the cardinality of the dataset

getChildren() - Method in class mulan.classifier.meta.HMCNode

Returns the children of the current node

getChildren() - Method in interface mulan.data.LabelNode

Gets the unmodifiable Set of child LabelNode of this node, if hierarchy exists.

getChildren() - Method in class mulan.data.LabelNodeImpl

getChildrenLabels() - Method in class mulan.classifier.meta.HMCNode

Returns a list of all children labels

getChildrenLabels() - Method in class mulan.data.LabelNodeImpl

Gets the children of a label

getClassifier() - Method in class mulan.classifier.meta.thresholding.Meta

Returns the classifier used to predict the number of labels/threshold

getClusterCentroids() - Method in class mulan.classifier.meta.ConstrainedKMeans

Gets the the cluster centroids

getClusterer() - Method in class mulan.classifier.meta.ClusteringBased

Returns the clustering approach

getClusterNominalCounts() - Method in class mulan.classifier.meta.ConstrainedKMeans

Returns for each cluster the frequency counts for the values of each nominal attribute

getClusterSizes() - Method in class mulan.classifier.meta.ConstrainedKMeans

Gets the number of instances in each cluster

getClusterStandardDevs() - Method in class mulan.classifier.meta.ConstrainedKMeans

Gets the standard deviations of the numeric attributes in each cluster

getConfidence() - Method in class mulan.evaluation.measure.LabelBasedAveragePrecision.ConfidenceActual

Returns the confidence

getConfidences() - Method in class mulan.classifier.MultiLabelOutput

Gets confidences of labels.

getConnectedNeuronsCount() - Method in class mulan.classifier.neural.model.Neuron

Gets the count of neurons connected to the output of this neuron instance.

getConvertNominalToBinary() - Method in class mulan.classifier.neural.MMPLearner

Gets a value indication whether conversion of nominal attributes from input data set to binary takes place prior to learning (and respectively making a prediction).

getCriticalValue() - Method in class mulan.data.ConditionalDependenceIdentifier

getCriticalValue() - Method in class mulan.data.GreedyLabelClustering

getCriticalValue() - Method in interface mulan.data.LabelPairsDependenceIdentifier

Returns a critical value

getCriticalValue() - Method in class mulan.data.UnconditionalChiSquareIdentifier

getDataSet() - Method in class mulan.data.MultiLabelInstances

Gets underlying Instances, which contains all data.

getDebug() - Method in class mulan.classifier.MultiLabelLearnerBase

Get whether debugging is turned on.

getDeltas() - Method in class mulan.classifier.neural.model.Neuron

Returns deltas of the Neuron.

getDepth(String) - Method in class mulan.data.MultiLabelInstances

Calculates the depth of a label, in the Hierarchy of the tree of labels.

getDescendantLabels() - Method in class mulan.classifier.meta.HMCNode

Returns all descendant labels

getDescendantLabels() - Method in interface mulan.data.LabelNode

Gets a Set of the names of descendant LabelNode of this node, if hierarchy exists.

getDescendantLabels() - Method in class mulan.data.LabelNodeImpl

getDistance() - Method in class mulan.classifier.meta.ConstrainedKMeans.bucketInstance

getDistances() - Method in class mulan.classifier.meta.ConstrainedKMeans.bucketInstance

getError() - Method in class mulan.classifier.neural.model.Neuron

Returns error term of the Neuron.

getFeatureAttributes() - Method in class mulan.data.MultiLabelInstances

Gets the Set of feature Attribute instances of this MultiLabelInstances instance.

getFeatureIndices() - Method in class mulan.data.MultiLabelInstances

Gets the array with indices of feature attributes stored in underlying Instances data set.

getFunctionParameters() - Method in class mulan.classifier.neural.ThresholdFunction

Returns parameters learned by the threshold function in last build.

getHeader() - Method in class mulan.classifier.meta.HMCNode

Returns the header information

getHiddenLayers() - Method in class mulan.classifier.neural.BPMLL

Gets an array defining topology of hidden layer of the underlying neural model.

getIdealValue() - Method in class mulan.evaluation.measure.AveragePrecision

getIdealValue() - Method in class mulan.evaluation.measure.Coverage

getIdealValue() - Method in class mulan.evaluation.measure.ExampleBasedAccuracy

getIdealValue() - Method in class mulan.evaluation.measure.ExampleBasedFMeasure

getIdealValue() - Method in class mulan.evaluation.measure.ExampleBasedPrecision

getIdealValue() - Method in class mulan.evaluation.measure.ExampleBasedRecall

getIdealValue() - Method in class mulan.evaluation.measure.ExampleBasedSpecificity

getIdealValue() - Method in class mulan.evaluation.measure.GeometricMeanAveragePrecision

getIdealValue() - Method in class mulan.evaluation.measure.LabelBasedAUC

getIdealValue() - Method in class mulan.evaluation.measure.LabelBasedFMeasure

getIdealValue() - Method in class mulan.evaluation.measure.LabelBasedPrecision

getIdealValue() - Method in class mulan.evaluation.measure.LabelBasedRecall

getIdealValue() - Method in class mulan.evaluation.measure.LabelBasedSpecificity

getIdealValue() - Method in class mulan.evaluation.measure.LossBasedBipartitionMeasureBase

getIdealValue() - Method in class mulan.evaluation.measure.LossBasedRankingMeasureBase

getIdealValue() - Method in class mulan.evaluation.measure.MeanAverageInterpolatedPrecision

getIdealValue() - Method in class mulan.evaluation.measure.MeanAveragePrecision

getIdealValue() - Method in interface mulan.evaluation.measure.Measure

Gets an 'ideal' value of a measure.

getIdealValue() - Method in class mulan.evaluation.measure.SubsetAccuracy

getIndex() - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator.Rank

Returns the index of the attribute

getInput() - Method in class mulan.classifier.neural.DataPair

Gets the input pattern.

getLabelAttributes() - Method in class mulan.data.MultiLabelInstances

Gets the Set of label Attribute instances of this MultiLabelInstances instance.

getLabelDepth() - Method in class mulan.data.MultiLabelInstances

Create a HashMap that contains every label, with its depth in the Hierarchical tree

getLabelDepthIndices() - Method in class mulan.data.MultiLabelInstances

Returns the depth of the labels

getLabelIndices() - Method in class mulan.classifier.meta.HMCNode

Returns the label indices

getLabelIndices() - Method in class mulan.data.MultiLabelInstances

getLabelNames() - Method in interface mulan.data.LabelsMetaData

Gets the names of all labels.

getLabelNames() - Method in class mulan.data.LabelsMetaDataImpl

getLabelNode(String) - Method in interface mulan.data.LabelsMetaData

Gets the LabelNode specified by label name.

getLabelNode(String) - Method in class mulan.data.LabelsMetaDataImpl

getLabelsMetaData() - Method in class mulan.data.MultiLabelInstances

Gets the LabelsMetaData instance, which contains descriptive meta-data about label attributes stored in underlying Instances data set.

getLabelsOrder() - Method in class mulan.data.MultiLabelInstances

getLayersCount() - Method in class mulan.classifier.neural.model.BasicNeuralNet

getLayersCount() - Method in interface mulan.classifier.neural.model.NeuralNet

Returns a total number of layers of the neural network.

getLayerUnits(int) - Method in class mulan.classifier.neural.model.BasicNeuralNet

getLayerUnits(int) - Method in interface mulan.classifier.neural.model.NeuralNet

Returns units of a particular layer of the neural network.

getLearningRate() - Method in class mulan.classifier.neural.BPMLL

Gets the learning rate.

getMax() - Method in class mulan.classifier.neural.model.ActivationFunction

Gets the maximum value the function can output.

getMax() - Method in class mulan.classifier.neural.model.ActivationLinear

getMax() - Method in class mulan.classifier.neural.model.ActivationTANH

getMean(String) - Method in class mulan.evaluation.MultipleEvaluation

Returns the mean value of a measure

getMeasure() - Method in class mulan.data.GreedyLabelClustering

getMeasures() - Method in class mulan.evaluation.Evaluation

Returns the evaluation measures

getMessage() - Method in exception mulan.core.ArgumentNullException

getMin() - Method in class mulan.classifier.neural.model.ActivationFunction

Gets the minimum value the function can output.

getMin() - Method in class mulan.classifier.neural.model.ActivationLinear

getMin() - Method in class mulan.classifier.neural.model.ActivationTANH

getModel() - Method in class mulan.classifier.meta.SubsetLearner

Returns a string representation of the model

getModel(String) - Method in class mulan.classifier.transformation.BinaryRelevance

Returns the model which corresponds to the label with labelName

getMultiLabelLearner() - Method in class mulan.data.GreedyLabelClustering

getName() - Method in class mulan.classifier.meta.HMCNode

Returns the name of a node

getName() - Method in interface mulan.data.LabelNode

Gets the name of the label this node represents.

getName() - Method in class mulan.data.LabelNodeImpl

getName() - Method in class mulan.evaluation.loss.ErrorSetSize

getName() - Method in class mulan.evaluation.loss.HammingLoss

getName() - Method in class mulan.evaluation.loss.HierarchicalLoss

getName() - Method in class mulan.evaluation.loss.IsError

getName() - Method in interface mulan.evaluation.loss.MultiLabelLossFunction

Returns the name of the loss function

getName() - Method in class mulan.evaluation.loss.OneError

getName() - Method in class mulan.evaluation.loss.OneMinusAveragePrecision

getName() - Method in class mulan.evaluation.loss.RankingLoss

getName() - Method in class mulan.evaluation.measure.AveragePrecision

getName() - Method in class mulan.evaluation.measure.Coverage

getName() - Method in class mulan.evaluation.measure.ExampleBasedAccuracy

getName() - Method in class mulan.evaluation.measure.ExampleBasedFMeasure

getName() - Method in class mulan.evaluation.measure.ExampleBasedPrecision

getName() - Method in class mulan.evaluation.measure.ExampleBasedRecall

getName() - Method in class mulan.evaluation.measure.ExampleBasedSpecificity

getName() - Method in class mulan.evaluation.measure.GeometricMeanAverageInterpolatedPrecision

getName() - Method in class mulan.evaluation.measure.GeometricMeanAveragePrecision

getName() - Method in class mulan.evaluation.measure.LossBasedBipartitionMeasureBase

getName() - Method in class mulan.evaluation.measure.LossBasedRankingMeasureBase

getName() - Method in class mulan.evaluation.measure.MacroAUC

getName() - Method in class mulan.evaluation.measure.MacroFMeasure

getName() - Method in class mulan.evaluation.measure.MacroPrecision

getName() - Method in class mulan.evaluation.measure.MacroRecall

getName() - Method in class mulan.evaluation.measure.MacroSpecificity

getName() - Method in class mulan.evaluation.measure.MeanAverageInterpolatedPrecision

getName() - Method in class mulan.evaluation.measure.MeanAveragePrecision

getName() - Method in interface mulan.evaluation.measure.Measure

Gets the name of a measure.

getName() - Method in class mulan.evaluation.measure.MicroAUC

getName() - Method in class mulan.evaluation.measure.MicroFMeasure

getName() - Method in class mulan.evaluation.measure.MicroPrecision

getName() - Method in class mulan.evaluation.measure.MicroRecall

getName() - Method in class mulan.evaluation.measure.MicroSpecificity

getName() - Method in class mulan.evaluation.measure.SubsetAccuracy

getNetInputSize() - Method in class mulan.classifier.neural.model.BasicNeuralNet

getNetInputSize() - Method in interface mulan.classifier.neural.model.NeuralNet

Gets the size/dimension of the input layer of the neural network.

getNetOutputSize() - Method in class mulan.classifier.neural.model.BasicNeuralNet

getNetOutputSize() - Method in interface mulan.classifier.neural.model.NeuralNet

Gets the size/dimension of the output layer of the neural network.

getNetwork() - Method in class mulan.classifier.neural.BPMLLAlgorithm

Returns the neural network which is learned/updated by the algorithm.

getNetworkError(double[], double[]) - Method in class mulan.classifier.neural.BPMLLAlgorithm

Returns the error of the neural network for given input.

getNeuronInput() - Method in class mulan.classifier.neural.model.Neuron

Returns an input value of the Neuron.

getNewLineSeparator() - Static method in class mulan.core.Util

Returns a correct new line separator string for the underlying operating system.

getNoClassifierEvals() - Method in class mulan.classifier.meta.HMC

Reurns number of classifier evaluations

getNoClassifierEvals() - Method in class mulan.classifier.meta.HOMER

Returns the number of classifier evaluations

getNoNodes() - Method in class mulan.classifier.meta.HMC

Returns the number of nodes

getNoNodes() - Method in class mulan.classifier.meta.HOMER

Returns the number of nodes

getNormalizeAttributes() - Method in class mulan.classifier.neural.BPMLL

Gets a value if normalization of nominal attributes should take place.

getNumChildren() - Method in class mulan.classifier.meta.HMCNode

Returns the number of children

getNumClusters() - Method in class mulan.classifier.meta.ConstrainedKMeans

gets the number of clusters to generate

getNumFolds() - Method in class mulan.data.ConditionalDependenceIdentifier

getNumFolds() - Method in class mulan.data.GreedyLabelClustering

getNumInstances() - Method in class mulan.data.MultiLabelInstances

Gets the number of instances

getNumLabels() - Method in interface mulan.data.LabelsMetaData

Gets the total number of LabelNode nodes.

getNumLabels() - Method in class mulan.data.LabelsMetaDataImpl

getNumLabels() - Method in class mulan.data.MultiLabelInstances

Gets the number of labels (label attributes)

getNumModels() - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

getNumModels() - Method in class mulan.classifier.meta.RAkEL

Returns the number of models

getOutput() - Method in class mulan.classifier.neural.DataPair

Gets the ideal/expected output pattern.

getOutput() - Method in class mulan.classifier.neural.model.BasicNeuralNet

getOutput() - Method in interface mulan.classifier.neural.model.NeuralNet

Returns the actual output of the neural network, which is a result of last processed input pattern.

getOutput() - Method in class mulan.classifier.neural.model.Neuron

Returns the output of the Neuron.

getOutputBoolean() - Method in class mulan.classifier.neural.DataPair

Gets the ideal/expected output pattern as boolean values.

getPair() - Method in class mulan.data.LabelsPair

getParent() - Method in interface mulan.data.LabelNode

Gets the parent LabelNode of this node if hierarchy exists.

getParent() - Method in class mulan.data.LabelNodeImpl

getPhiHistogram() - Method in class mulan.data.Statistics

Calculates a histogram of phi correlations

getRanking() - Method in class mulan.classifier.MultiLabelOutput

Gets ranking of labels.

getRootLabels() - Method in interface mulan.data.LabelsMetaData

Gets the unmodifiable Set of root LabelNode nodes of label attributes hierarchy.

getRootLabels() - Method in class mulan.data.LabelsMetaDataImpl

getSamplingPercentage() - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

Returns the sampling percentage

getScore() - Method in class mulan.data.LabelsPair

getScore() - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator.Rank

Returns the score of the attribute

getSeed() - Method in class mulan.data.ConditionalDependenceIdentifier

getSingleLabelLearner() - Method in class mulan.data.GreedyLabelClustering

getSizeOfSubset() - Method in class mulan.classifier.meta.RAkEL

Returns the size of the subsets

getSizeOfSubset() - Method in class mulan.classifier.meta.RAkELd

Returns the size of the subsets

getSquaredError() - Method in class mulan.classifier.meta.ConstrainedKMeans

Gets the squared error for all clusters

getStandardVoting() - Method in class mulan.classifier.transformation.CalibratedLabelRanking

Get whether standard voting is turned on.

getSubsets() - Method in class mulan.data.LabelSet

Constructs all subsets of a labelset (apart from the empty one).

getTechnicalInformation() - Method in class mulan.classifier.lazy.BRkNN

getTechnicalInformation() - Method in class mulan.classifier.lazy.IBLR_ML

getTechnicalInformation() - Method in class mulan.classifier.lazy.MLkNN

getTechnicalInformation() - Method in class mulan.classifier.meta.ClusteringBased

getTechnicalInformation() - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

getTechnicalInformation() - Method in class mulan.classifier.meta.HMC

getTechnicalInformation() - Method in class mulan.classifier.meta.HMCNode

getTechnicalInformation() - Method in class mulan.classifier.meta.HOMER

getTechnicalInformation() - Method in class mulan.classifier.meta.RAkEL

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.classifier.meta.RAkELd

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.classifier.meta.SubsetLearner

getTechnicalInformation() - Method in class mulan.classifier.meta.thresholding.ExampleBasedFMeasureOptimizer

getTechnicalInformation() - Method in class mulan.classifier.meta.thresholding.MetaLabeler

getTechnicalInformation() - Method in class mulan.classifier.meta.thresholding.MLPTO

getTechnicalInformation() - Method in class mulan.classifier.meta.thresholding.OneThreshold

getTechnicalInformation() - Method in class mulan.classifier.meta.thresholding.RCut

getTechnicalInformation() - Method in class mulan.classifier.meta.thresholding.SCut

getTechnicalInformation() - Method in class mulan.classifier.meta.thresholding.ThresholdPrediction

getTechnicalInformation() - Method in class mulan.classifier.MultiLabelLearnerBase

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.classifier.neural.BPMLL

getTechnicalInformation() - Method in class mulan.classifier.neural.MMPLearner

getTechnicalInformation() - Method in class mulan.classifier.transformation.AdaBoostMH

getTechnicalInformation() - Method in class mulan.classifier.transformation.CalibratedLabelRanking

getTechnicalInformation() - Method in class mulan.classifier.transformation.ClassifierChain

getTechnicalInformation() - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

getTechnicalInformation() - Method in class mulan.classifier.transformation.EnsembleOfPrunedSets

getTechnicalInformation() - Method in class mulan.classifier.transformation.MultiLabelStacking

getTechnicalInformation() - Method in class mulan.classifier.transformation.PPT

getTechnicalInformation() - Method in class mulan.classifier.transformation.PrunedSets

getTechnicalInformation() - Method in class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.data.IterativeStratification

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.data.LabelPowersetStratification

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.data.Statistics

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.experiments.Experiment

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTechnicalInformation() - Method in class mulan.experiments.ICDM08EnsembleOfPrunedSets

getTechnicalInformation() - Method in class mulan.experiments.ICTAI2010

getTechnicalInformation() - Method in class mulan.experiments.MachineLearning09IBLR

getTechnicalInformation() - Method in class mulan.experiments.PatternRecognition07MLkNN

getThreshold() - Method in class mulan.classifier.meta.thresholding.OneThreshold

Returns the calculated threshold

getTopkCorrelated() - Method in class mulan.classifier.transformation.MultiLabelStacking

Returns top k correlated labels

getTotalUsedTrainInsts() - Method in class mulan.classifier.meta.HMC

Returns number of total instances used

getTotalUsedTrainInsts() - Method in class mulan.classifier.meta.HOMER

Returns the total number of instances used for training

getTrainingEpochs() - Method in class mulan.classifier.neural.BPMLL

Gets number of training epochs.

getTrainingEpochs() - Method in class mulan.classifier.neural.MMPLearner

Gets number of training epochs.

getTransformedFormat() - Method in class mulan.transformations.LabelPowersetTransformation

Returns the format of the transformed instances

getValue() - Method in class mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase

getValue() - Method in class mulan.evaluation.measure.GeometricMeanAverageInterpolatedPrecision

getValue() - Method in class mulan.evaluation.measure.GeometricMeanAveragePrecision

getValue() - Method in class mulan.evaluation.measure.MacroAUC

getValue(int) - Method in class mulan.evaluation.measure.MacroAUC

Returns the AUC for a particular label

getValue(int) - Method in interface mulan.evaluation.measure.MacroAverageMeasure

Returns the value of a macro average measure for a particular label

getValue() - Method in class mulan.evaluation.measure.MacroFMeasure

getValue(int) - Method in class mulan.evaluation.measure.MacroFMeasure

Returns the F-Measure for a label

getValue() - Method in class mulan.evaluation.measure.MacroPrecision

getValue(int) - Method in class mulan.evaluation.measure.MacroPrecision

Returns the precision for a label

getValue() - Method in class mulan.evaluation.measure.MacroRecall

getValue(int) - Method in class mulan.evaluation.measure.MacroRecall

Returns the recall for a label

getValue() - Method in class mulan.evaluation.measure.MacroSpecificity

getValue(int) - Method in class mulan.evaluation.measure.MacroSpecificity

Returns the specificity for a label

getValue() - Method in class mulan.evaluation.measure.MeanAverageInterpolatedPrecision

getValue(int) - Method in class mulan.evaluation.measure.MeanAverageInterpolatedPrecision

Returns the average interpolated precision for a label

getValue() - Method in class mulan.evaluation.measure.MeanAveragePrecision

Calculates map using multiple calls to MeanAveragePrecision.getValue(int).

getValue(int) - Method in class mulan.evaluation.measure.MeanAveragePrecision

Returns the average precision for a label.

getValue() - Method in interface mulan.evaluation.measure.Measure

Gets the value of a measure.

getValue() - Method in class mulan.evaluation.measure.MicroAUC

getValue() - Method in class mulan.evaluation.measure.MicroFMeasure

getValue() - Method in class mulan.evaluation.measure.MicroPrecision

getValue() - Method in class mulan.evaluation.measure.MicroRecall

getValue() - Method in class mulan.evaluation.measure.MicroSpecificity

getValue() - Method in class mulan.evaluation.measure.RankingMeasureBase

getWeights() - Method in class mulan.classifier.neural.model.Neuron

Returns weights of the Neuron.

getWeightsDecayCost() - Method in class mulan.classifier.neural.BPMLLAlgorithm

Returns the value of weights decay cost term used for regularization.

getWeightsDecayRegularization() - Method in class mulan.classifier.neural.BPMLL

Gets a value of the regularization cost term for weights decay.

globalInfo() - Method in class mulan.classifier.lazy.BRkNN

globalInfo() - Method in class mulan.classifier.lazy.IBLR_ML

globalInfo() - Method in class mulan.classifier.lazy.MLkNN

globalInfo() - Method in class mulan.classifier.meta.ClusteringBased

globalInfo() - Method in class mulan.classifier.meta.ConstrainedKMeans

Returns a string describing this clusterer

globalInfo() - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

globalInfo() - Method in class mulan.classifier.meta.HMC

globalInfo() - Method in class mulan.classifier.meta.HMCNode

globalInfo() - Method in class mulan.classifier.meta.HOMER

globalInfo() - Method in class mulan.classifier.meta.RAkEL

Returns a string describing classifier

globalInfo() - Method in class mulan.classifier.meta.RAkELd

Returns a string describing classifier

globalInfo() - Method in class mulan.classifier.meta.SubsetLearner

globalInfo() - Method in class mulan.classifier.meta.thresholding.ExampleBasedFMeasureOptimizer

globalInfo() - Method in class mulan.classifier.meta.thresholding.MetaLabeler

globalInfo() - Method in class mulan.classifier.meta.thresholding.MLPTO

globalInfo() - Method in class mulan.classifier.meta.thresholding.OneThreshold

globalInfo() - Method in class mulan.classifier.meta.thresholding.RCut

globalInfo() - Method in class mulan.classifier.meta.thresholding.SCut

globalInfo() - Method in class mulan.classifier.meta.thresholding.ThresholdPrediction

globalInfo() - Method in class mulan.classifier.MultiLabelLearnerBase

Returns a string describing the multi-label learner.

globalInfo() - Method in class mulan.classifier.neural.BPMLL

globalInfo() - Method in class mulan.classifier.neural.MMPLearner

globalInfo() - Method in class mulan.classifier.transformation.AdaBoostMH

Returns a string describing the classifier.

globalInfo() - Method in class mulan.classifier.transformation.CalibratedLabelRanking

Returns a string describing the classifier.

globalInfo() - Method in class mulan.classifier.transformation.ClassifierChain

Returns a string describing the classifier.

globalInfo() - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

Returns a string describing classifier.

globalInfo() - Method in class mulan.classifier.transformation.EnsembleOfPrunedSets

Returns a string describing classifier

globalInfo() - Method in class mulan.classifier.transformation.MultiLabelStacking

Returns a string describing the classifier.

globalInfo() - Method in class mulan.classifier.transformation.PPT

Returns a string describing classifier

globalInfo() - Method in class mulan.classifier.transformation.PrunedSets

Returns a string describing classifier

globalInfo() - Method in class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

Returns a string describing the classifier.

globalInfo() - Method in class mulan.core.MulanJavadoc

Returns global information about the class

globalInfo() - Method in class mulan.data.Statistics

Returns a string describing this class.

GreedyLabelClustering - Class in mulan.data

A class for clustering dependent label pairs into disjoint subsets.

GreedyLabelClustering(MultiLabelLearner, Classifier, LabelPairsDependenceIdentifier) - Constructor for class mulan.data.GreedyLabelClustering

Initialize the GreedyLabelClustering with multilabel and single label learners and a method for labels dependence identification.

H

hammingDifference(LabelSet) - Method in class mulan.data.LabelSet

Calculates the Hamming Distance between the current labelset and another labelset.

HammingLoss - Class in mulan.evaluation.loss

Implementation of the hamming loss function.

HammingLoss() - Constructor for class mulan.evaluation.loss.HammingLoss

HammingLoss - Class in mulan.evaluation.measure

Implementation of the Hamming loss function.

HammingLoss() - Constructor for class mulan.evaluation.measure.HammingLoss

Creates an instance of this object based on the corresponding loss function

hasBipartition() - Method in class mulan.classifier.MultiLabelOutput

Determines whether the MultiLabelOutput has bipartition of labels.

hasChildren() - Method in class mulan.classifier.meta.HMCNode

Checks whether the node has children

hasChildren() - Method in interface mulan.data.LabelNode

Determines whether the LabelNode has child nodes.

hasChildren() - Method in class mulan.data.LabelNodeImpl

hasConfidences() - Method in class mulan.classifier.MultiLabelOutput

Determines whether the MultiLabelOutput has confidences of labels.

hashCode() - Method in class mulan.classifier.MultiLabelOutput

hashCode() - Method in class mulan.data.LabelNodeImpl

The hash code is computed based on label name attribute, which defines the identity of the LabelNodeImpl node.

hashCode() - Method in class mulan.data.LabelSet

hashCode() - Method in class mulan.data.LabelsPair

hasMissingLabels(Instance) - Method in class mulan.data.MultiLabelInstances

Method that checks whether an instance has missing labels

hasParent() - Method in interface mulan.data.LabelNode

Determines whether the LabelNode has a parent node in a hierarchy.

hasParent() - Method in class mulan.data.LabelNodeImpl

hasRanking() - Method in class mulan.classifier.MultiLabelOutput

Determines whether the MultiLabelOutput has ranking of labels.

HierarchicalLoss - Class in mulan.evaluation.loss

Implementation of the hierarchical loss function.

HierarchicalLoss(MultiLabelInstances) - Constructor for class mulan.evaluation.loss.HierarchicalLoss

Creates a new instance of this class

HierarchicalLoss - Class in mulan.evaluation.measure

Implementation of the Hierarchical loss measure.

HierarchicalLoss(MultiLabelInstances) - Constructor for class mulan.evaluation.measure.HierarchicalLoss

Creates an instance of this object based on the corresponding loss function

HierarchyBuilder - Class in mulan.classifier.meta

Class that builds a hierarchy on flat lables of given mulltilabel data.

HierarchyBuilder(int, HierarchyBuilder.Method) - Constructor for class mulan.classifier.meta.HierarchyBuilder

Constructs a new istance based on given number of partitions and method

HierarchyBuilder.Method - Enum in mulan.classifier.meta

The different types of distributing labels to children nodes

highest(double[][], int) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

highest score combination approach

HMC - Class in mulan.classifier.meta

Class that implements a Hierarchical Multilabel classifier (HMC).

HMC() - Constructor for class mulan.classifier.meta.HMC

Default constructor

HMC(MultiLabelLearner) - Constructor for class mulan.classifier.meta.HMC

Constructs a new instance

HMCNode - Class in mulan.classifier.meta

Class that implements a node for the HMC

HMCNode(String, MultiLabelLearner) - Constructor for class mulan.classifier.meta.HMCNode

Creates a new instance with the given name and learner

HOMER - Class in mulan.classifier.meta

Class implementing the Hierarchy Of Multi-labEl leaRners algorithm.

HOMER() - Constructor for class mulan.classifier.meta.HOMER

Default constructor

HOMER(MultiLabelLearner, int, HierarchyBuilder.Method) - Constructor for class mulan.classifier.meta.HOMER

Creates a new instance based on given multi-label learner, number of children and partitioning method

I

IBLR_ML - Class in mulan.classifier.lazy

This class is an implementation of the "IBLR-ML" and "IBLR-ML+" methods for the MULAN package.

IBLR_ML() - Constructor for class mulan.classifier.lazy.IBLR_ML

Default constructor uses 10 NN

IBLR_ML(int) - Constructor for class mulan.classifier.lazy.IBLR_ML

Constructor that sets the number of neighbors

IBLR_ML(int, boolean) - Constructor for class mulan.classifier.lazy.IBLR_ML

Full constructor

ICDM08EnsembleOfPrunedSets - Class in mulan.experiments

Class replicating an experiment from a published paper

ICDM08EnsembleOfPrunedSets() - Constructor for class mulan.experiments.ICDM08EnsembleOfPrunedSets

ICTAI2010 - Class in mulan.experiments

Class replicating an experiment from a published paper

ICTAI2010() - Constructor for class mulan.experiments.ICTAI2010

Ignore - Class in mulan.transformations.multiclass

Class that implement the Ignore transformation method

Ignore() - Constructor for class mulan.transformations.multiclass.Ignore

IncludeLabelsClassifier - Class in mulan.classifier.transformation

A multilabel classifier based on the include labels transformation.

IncludeLabelsClassifier(Classifier) - Constructor for class mulan.classifier.transformation.IncludeLabelsClassifier

Constructor that initializes a new learner with the given base classifier

IncludeLabelsTransformation - Class in mulan.transformations

Class that implements the PT6 transformation

IncludeLabelsTransformation() - Constructor for class mulan.transformations.IncludeLabelsTransformation

InformationRetrievalMeasures - Class in mulan.evaluation.measure

Class for computing various information retrieval measures.

InformationRetrievalMeasures() - Constructor for class mulan.evaluation.measure.InformationRetrievalMeasures

initializeMetaLevel(MultiLabelInstances, Classifier, boolean, double, ASEvaluation) - Method in class mulan.classifier.transformation.MultiLabelStacking

Initializes all the parameters used in the meta-level.

intersection(LabelSet, LabelSet) - Static method in class mulan.data.LabelSet

InvalidDataException - Exception in mulan.classifier

Exception thrown by the MultiLabelLearner when presented with invalid data or data which can not be processed by the specific learner type.

InvalidDataException(String) - Constructor for exception mulan.classifier.InvalidDataException

Creates a new instance of InvalidDataException with the specified detail message.

InvalidDataException(String, Throwable) - Constructor for exception mulan.classifier.InvalidDataException

Creates a new instance of InvalidDataException with the specified detail message and nested exception.

InvalidDataFormatException - Exception in mulan.data

The exception is thrown when format of the data is not valid.

InvalidDataFormatException(String) - Constructor for exception mulan.data.InvalidDataFormatException

Creates a new instance of InvalidDataFormatException with detail mesage.

InvalidDataFormatException(String, Throwable) - Constructor for exception mulan.data.InvalidDataFormatException

Creates a new instance of InvalidDataFormatException with detail message and nested exception.

IsError - Class in mulan.evaluation.loss

Implementation of the IsError loss function, which is simply the indicator of whether the induced ranking is perfect or not.

IsError() - Constructor for class mulan.evaluation.loss.IsError

IsError - Class in mulan.evaluation.measure

Measure based on the IsError ranking loss function

IsError() - Constructor for class mulan.evaluation.measure.IsError

Creates an instance of this object based on the corresponding loss function

isHierarchy() - Method in interface mulan.data.LabelsMetaData

Determines if there is a hierarchy defined between labels.

isHierarchy() - Method in class mulan.data.LabelsMetaDataImpl

isInternalSubsetLearnerDebug() - Method in class mulan.data.GreedyLabelClustering

isModelInitialized() - Method in class mulan.classifier.MultiLabelLearnerBase

Gets whether learner's model is initialized by MultiLabelLearnerBase.build(MultiLabelInstances).

isSelectDiverseModels() - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

isUpdatable() - Method in class mulan.classifier.lazy.MultiLabelKNN

isUpdatable() - Method in interface mulan.classifier.MultiLabelLearner

Returns value indicating if learner is updatable, so if learner is able to perform on-line learning.

isUpdatable() - Method in class mulan.classifier.MultiLabelLearnerBase

isUpdatable() - Method in class mulan.classifier.neural.MMPLearner

isUseSubsetLearnerCache() - Method in class mulan.data.GreedyLabelClustering

IterativeStratification - Class in mulan.data

Class for stratifying data based on the iterative stratification method

IterativeStratification() - Constructor for class mulan.data.IterativeStratification

Default constructor

IterativeStratification(long) - Constructor for class mulan.data.IterativeStratification

Constructor setting a specific random seed

K

kFoldsCV - Variable in class mulan.classifier.meta.thresholding.Meta

the number of folds for cross validation

L

LabelBasedAUC - Class in mulan.evaluation.measure

Implementation of the label-based macro precision measure.

LabelBasedAUC(int) - Constructor for class mulan.evaluation.measure.LabelBasedAUC

Creates a new instance of this class

LabelBasedAveragePrecision - Class in mulan.evaluation.measure

LabelBasedAveragePrecision(int) - Constructor for class mulan.evaluation.measure.LabelBasedAveragePrecision

Creates a new instance of this class

LabelBasedAveragePrecision.ConfidenceActual - Class in mulan.evaluation.measure

Class that stores a confidence and a ground truth for one label/example

LabelBasedAveragePrecision.ConfidenceActual(double, boolean) - Constructor for class mulan.evaluation.measure.LabelBasedAveragePrecision.ConfidenceActual

Creates a new instance of this class

LabelBasedBipartitionMeasureBase - Class in mulan.evaluation.measure

Base class for label-based bipartition measures

LabelBasedBipartitionMeasureBase(int) - Constructor for class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

Creates a new instance of this class

LabelBasedFMeasure - Class in mulan.evaluation.measure

Base implementation of the label-based macro/micro f-measures.

LabelBasedFMeasure(int) - Constructor for class mulan.evaluation.measure.LabelBasedFMeasure

Constructs a new object with given number of labels

LabelBasedFMeasure(int, double) - Constructor for class mulan.evaluation.measure.LabelBasedFMeasure

Constructs a new object with given number of labels and beta parameter

LabelBasedPrecision - Class in mulan.evaluation.measure

Base implementation of the label-based macro/micro precision measures.

LabelBasedPrecision(int) - Constructor for class mulan.evaluation.measure.LabelBasedPrecision

Constructs a new object with given number of labels

LabelBasedRecall - Class in mulan.evaluation.measure

Common class for the micro/macro label-based recall measures.

LabelBasedRecall(int) - Constructor for class mulan.evaluation.measure.LabelBasedRecall

Constructs a new object with given number of labels

LabelBasedSpecificity - Class in mulan.evaluation.measure

Common class for the micro/macro label-based recall measures.

LabelBasedSpecificity(int) - Constructor for class mulan.evaluation.measure.LabelBasedSpecificity

Constructs a new object with given number of labels

LabelClustering - Interface in mulan.data

An interface for various label clustering algorithms.

labelCombCount() - Method in class mulan.data.Statistics

returns the HashMap containing the distinct labelsets and their frequencies

labelFrequency(LabelSet) - Method in class mulan.data.Statistics

returns the frequency of a labelset in the dataset

labelIndices - Variable in class mulan.classifier.MultiLabelLearnerBase

An array containing the indexes of the label attributes within the Instances object of the training data in increasing order.

labelIndices - Variable in class mulan.transformations.multiclass.MultiClassTransformationBase

the array with the label indices

LabelNode - Interface in mulan.data

Represents a label attribute as a node in the labels hierarchy.

LabelNodeImpl - Class in mulan.data

Implementation of LabelNode, representing a label attribute and its connection within a hierarchy of labels.

LabelNodeImpl(String) - Constructor for class mulan.data.LabelNodeImpl

Creates a new instance of LabelNodeImpl.

LabelPairsDependenceIdentifier - Interface in mulan.data

An interface for various types of dependency identification between pairs of labels. .

LabelPowerset - Class in mulan.classifier.transformation

Class that implements a label powerset classifier

LabelPowerset(Classifier) - Constructor for class mulan.classifier.transformation.LabelPowerset

Conststructor that initializes the learner with a base classifier

LabelPowersetAttributeEvaluator - Class in mulan.dimensionalityReduction

Performs attribute evaluation using the label powerset transformation.

LabelPowersetAttributeEvaluator(ASEvaluation, MultiLabelInstances) - Constructor for class mulan.dimensionalityReduction.LabelPowersetAttributeEvaluator

Constructor that uses an evaluator on a multi-label dataset

LabelPowersetStratification - Class in mulan.data

Class for stratifying data based on label combinations

LabelPowersetStratification() - Constructor for class mulan.data.LabelPowersetStratification

Default constructor

LabelPowersetStratification(int) - Constructor for class mulan.data.LabelPowersetStratification

Constructor setting the random seed

LabelPowersetTransformation - Class in mulan.transformations

Class that implement the Label powerset (LP) transformation method

LabelPowersetTransformation() - Constructor for class mulan.transformations.LabelPowersetTransformation

LABELS_SCHEMA_NAMESPACE - Static variable in class mulan.data.LabelsBuilder

The namespace of the schema for label representation

LabelsBuilder - Class in mulan.data

The LabelsBuilder is responsible for creation of LabelsMetaDataImpl instance from specified XML file source.

LabelsBuilder() - Constructor for class mulan.data.LabelsBuilder

LabelsBuilderException - Exception in mulan.data

Exception is raised by LabelsBuilder to indicate an error when creating LabelsMetaDataImpl instance form specified source.

LabelsBuilderException(String) - Constructor for exception mulan.data.LabelsBuilderException

Creates a new LabelsBuilderException instance.

LabelsBuilderException(String, Throwable) - Constructor for exception mulan.data.LabelsBuilderException

Creates a new instance of LabelsBuilderException with the specified detail message and nested exception.

LabelSet - Class in mulan.data

Class that handles labelsets

LabelSet(double[]) - Constructor for class mulan.data.LabelSet

Initializes an object based on an array of doubles containing 0/1

labelSet - Variable in class mulan.data.LabelSet

The set is represented internally as an array of integers.

LabelsetPruning - Class in mulan.classifier.transformation

Common functionality class for the PPT and PS algorithms

LabelsetPruning(Classifier, int) - Constructor for class mulan.classifier.transformation.LabelsetPruning

Constructor that initializes learner with base algorithm and main parameter

labelSets() - Method in class mulan.data.Statistics

returns a set with the distinct labelsets of the dataset

labelsFromConfidences2(double[]) - Method in class mulan.classifier.lazy.BRkNN

used for BRknn-a

labelsFromConfidences3(double[]) - Method in class mulan.classifier.lazy.BRkNN

used for BRkNN-b (break ties arbitrarily)

LabelsMetaData - Interface in mulan.data

Represents meta data about label attributes and their structure.

LabelsMetaDataImpl - Class in mulan.data

Implementation of LabelsMetaData info about labels and their structure.

LabelsMetaDataImpl() - Constructor for class mulan.data.LabelsMetaDataImpl

Creates a new instance of LabelsMetaDataImpl.

LabelsPair - Class in mulan.data

Class for handling label pairs along with their 'dependence' scores.

LabelsPair(int[], double) - Constructor for class mulan.data.LabelsPair

Initialize a labels pair using an array of two int values.

learn(double[], double[], double) - Method in class mulan.classifier.neural.BPMLLAlgorithm

Performs one learning step with given input pattern and expected output values.

LearnerException - Exception in mulan.classifier

Represents a root base class for exceptions thrown by learners.

LearnerException(String) - Constructor for exception mulan.classifier.LearnerException

Creates a new instance of LearnerException with the specified detail message.

LearnerException(String, Throwable) - Constructor for exception mulan.classifier.LearnerException

Creates a new instance of LearnerException with the specified detail message and nested exception.

listOptions() - Method in class mulan.classifier.meta.ConstrainedKMeans

Returns an enumeration describing the available options.

lnn - Variable in class mulan.classifier.lazy.MultiLabelKNN

Class implementing the brute force search algorithm for nearest neighbor search.

LossBasedBipartitionMeasureBase - Class in mulan.evaluation.measure

LossBasedBipartitionMeasureBase(BipartitionLossFunction) - Constructor for class mulan.evaluation.measure.LossBasedBipartitionMeasureBase

Creates a loss-based bipartition measure

LossBasedRankingMeasureBase - Class in mulan.evaluation.measure

LossBasedRankingMeasureBase(RankingLossFunction) - Constructor for class mulan.evaluation.measure.LossBasedRankingMeasureBase

Creates a loss-based ranking measure

lowest(double[][], int) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

lowest score combination approach

M

m_Predictions - Variable in class mulan.evaluation.measure.LabelBasedAUC

The predictions for each label

MachineLearning09IBLR - Class in mulan.experiments

Class replicating an experiment from a published paper

MachineLearning09IBLR() - Constructor for class mulan.experiments.MachineLearning09IBLR

MacroAUC - Class in mulan.evaluation.measure

Implementation of the macro-averaged AUC measure.

MacroAUC(int) - Constructor for class mulan.evaluation.measure.MacroAUC

Creates a new instance of this class

MacroAverageMeasure - Interface in mulan.evaluation.measure

Interface for macro average measures

MacroFMeasure - Class in mulan.evaluation.measure

Implementation of the macro-averaged f measure.

MacroFMeasure(int) - Constructor for class mulan.evaluation.measure.MacroFMeasure

Constructs a new object with given number of labels and beta=1

MacroFMeasure(int, double) - Constructor for class mulan.evaluation.measure.MacroFMeasure

Full constructor

MacroPrecision - Class in mulan.evaluation.measure

Implementation of the macro-averaged precision measure.

MacroPrecision(int) - Constructor for class mulan.evaluation.measure.MacroPrecision

Constructs a new object with given number of labels

MacroRecall - Class in mulan.evaluation.measure

Implementation of the macro-averaged recall measure.

MacroRecall(int) - Constructor for class mulan.evaluation.measure.MacroRecall

Constructs a new object with given number of labels

MacroSpecificity - Class in mulan.evaluation.measure

Implementation of the macro-averaged recall measure.

MacroSpecificity(int) - Constructor for class mulan.evaluation.measure.MacroSpecificity

Constructs a new object with given number of labels and strictness

main(String[]) - Static method in class mulan.core.MulanJavadoc

Command line interface

main(String[]) - Static method in class mulan.data.ConverterLibSVM

Command line interface for the converter

main(String[]) - Static method in class mulan.experiments.ICDM08EnsembleOfPrunedSets

Main class

main(String[]) - Static method in class mulan.experiments.ICTAI2010

Main class

main(String[]) - Static method in class mulan.experiments.MachineLearning09IBLR

Main class

main(String[]) - Static method in class mulan.experiments.PatternRecognition07MLkNN

Main class

makeCopy() - Method in interface mulan.classifier.MultiLabelLearner

Creates a deep copy of the given learner using serialization.

makeCopy() - Method in class mulan.classifier.MultiLabelLearnerBase

makeCopy() - Method in interface mulan.evaluation.measure.Measure

Creates a deep copy of the given measure using serialization.

makeCopy() - Method in class mulan.evaluation.measure.MeasureBase

makePrediction(Instance) - Method in interface mulan.classifier.MultiLabelLearner

Returns the prediction of the learner for a given input Instance.

makePrediction(Instance) - Method in class mulan.classifier.MultiLabelLearnerBase

makePredictionInternal(Instance) - Method in class mulan.classifier.lazy.BRkNN

weka Ibk style prediction

makePredictionInternal(Instance) - Method in class mulan.classifier.lazy.IBLR_ML

makePredictionInternal(Instance) - Method in class mulan.classifier.lazy.MLkNN

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.ClusteringBased

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

Makes classification prediction using constructed ensemble of Subset models.

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.HMC

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.HMCNode

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.HOMER

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.RAkEL

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.RAkELd

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.SubsetLearner

We make a prediction using a different method depending on whether the split has one or more labels

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.thresholding.ExampleBasedFMeasureOptimizer

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.thresholding.MetaLabeler

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.thresholding.MLPTO

Produces the optimal bipartition output from a probabilistic multi label output for a predefined loss function.

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.thresholding.OneThreshold

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.thresholding.RCut

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.thresholding.SCut

makePredictionInternal(Instance) - Method in class mulan.classifier.meta.thresholding.ThresholdPrediction

makePredictionInternal(Instance) - Method in class mulan.classifier.MultiLabelLearnerBase

Learner specific implementation for predicting on specified data based on trained model.

makePredictionInternal(Instance) - Method in class mulan.classifier.neural.BPMLL

makePredictionInternal(Instance) - Method in class mulan.classifier.neural.MMPLearner

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.BinaryRelevance

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.CalibratedLabelRanking

This method does a prediction for an instance with the values of label missing Temporary included to switch between standard voting and qweighted multilabel voting

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.ClassifierChain

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.EnsembleOfPrunedSets

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.IncludeLabelsClassifier

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.LabelPowerset

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.MultiClassLearner

makePredictionInternal(Instance) - Method in class mulan.classifier.transformation.MultiLabelStacking

makePredictionQW(Instance) - Method in class mulan.classifier.transformation.CalibratedLabelRanking

This method does a prediction for an instance with the values of label missing according to QWeighted algorithm for Multilabel Classification (QCMLPP2), which is described in : Loza Mencia, E., Park, S.

makePredictionsBasedOnConfidences - Variable in class mulan.classifier.transformation.LabelPowerset

Whether the method introduced by the PPT algorithm will be used to actually get the 1/0 output bipartition based on the confidences (requires a threshold)

makePredictionStandard(Instance) - Method in class mulan.classifier.transformation.CalibratedLabelRanking

This method does a prediction for an instance with the values of label missing

MAX - Static variable in class mulan.classifier.neural.model.ActivationTANH

Maximum value of function

MeanAverageInterpolatedPrecision - Class in mulan.evaluation.measure

Implementation of MAiP (Mean Average Interpolated Precision)

MeanAverageInterpolatedPrecision(int, int) - Constructor for class mulan.evaluation.measure.MeanAverageInterpolatedPrecision

Constructor

MeanAveragePrecision - Class in mulan.evaluation.measure

Implementation of MAP (Mean Average Precision)

MeanAveragePrecision(int) - Constructor for class mulan.evaluation.measure.MeanAveragePrecision

Creates a new instance of this class

Measure - Interface in mulan.evaluation.measure

Interface for a measure, used to evaluate the performance of a multi-label learner on when performing multi-label learning task.

MeasureBase - Class in mulan.evaluation.measure

MeasureBase() - Constructor for class mulan.evaluation.measure.MeasureBase

Meta - Class in mulan.classifier.meta.thresholding

Base class for instance-based prediction of a bipartition from the labels' scores

Meta(MultiLabelLearner, Classifier, String) - Constructor for class mulan.classifier.meta.thresholding.Meta

Constructor that initializes the learner

metaDatasetChoice - Variable in class mulan.classifier.meta.thresholding.Meta

the type for constructing the meta dataset

metaDataTest - Variable in class mulan.classifier.transformation.CalibratedLabelRanking

headers of the training sets of the one vs one models

MetaLabeler - Class in mulan.classifier.meta.thresholding

Class implementing the MetaLabeler algorithm.

MetaLabeler() - Constructor for class mulan.classifier.meta.thresholding.MetaLabeler

Default constructor

MetaLabeler(MultiLabelLearner, Classifier, String, String) - Constructor for class mulan.classifier.meta.thresholding.MetaLabeler

Constructor that initializes the learner

MicroAUC - Class in mulan.evaluation.measure

Implementation of the micro-averaged AUC measure.

MicroAUC(int) - Constructor for class mulan.evaluation.measure.MicroAUC

Creates a new instance of this class

MicroFMeasure - Class in mulan.evaluation.measure

Implementation of the micro-averaged precision measure.

MicroFMeasure(int) - Constructor for class mulan.evaluation.measure.MicroFMeasure

Constructs a new object with given number of labels and beta=1

MicroFMeasure(int, double) - Constructor for class mulan.evaluation.measure.MicroFMeasure

Full constructor

MicroPrecision - Class in mulan.evaluation.measure

Implementation of the micro-averaged precision measure.

MicroPrecision(int) - Constructor for class mulan.evaluation.measure.MicroPrecision

Constructs a new object with given number of labels

MicroRecall - Class in mulan.evaluation.measure

Implementation of the micro-averaged recall measure.

MicroRecall(int) - Constructor for class mulan.evaluation.measure.MicroRecall

Constructs a new object with given number of labels

MicroSpecificity - Class in mulan.evaluation.measure

Implementation of the micro-averaged recall measure.

MicroSpecificity(int) - Constructor for class mulan.evaluation.measure.MicroSpecificity

Constructs a new object with given number of labels

MIN - Static variable in class mulan.classifier.neural.model.ActivationTANH

Minimum value of function

MLkNN - Class in mulan.classifier.lazy

Class implementing the ML-kNN (Multi-Label k Nearest Neighbours) algorithm.

MLkNN(int, double) - Constructor for class mulan.classifier.lazy.MLkNN

MLkNN() - Constructor for class mulan.classifier.lazy.MLkNN

The default constructor

MLPTO - Class in mulan.classifier.meta.thresholding

Class that implements the Multi Label Probabilistic Threshold Optimizer (MLTPTO).

MLPTO() - Constructor for class mulan.classifier.meta.thresholding.MLPTO

Default constructor

MLPTO(MultiLabelLearner, ExampleBasedBipartitionMeasureBase) - Constructor for class mulan.classifier.meta.thresholding.MLPTO

MMPLearner - Class in mulan.classifier.neural

Implementation of Multiclass Multilabel Perceptrons learner.

MMPLearner() - Constructor for class mulan.classifier.neural.MMPLearner

Default constructor using RankingLoss and uniform update

MMPLearner(RankingLossFunction, MMPUpdateRuleType) - Constructor for class mulan.classifier.neural.MMPLearner

Creates a new instance of MMPLearner.

MMPLearner(RankingLossFunction, MMPUpdateRuleType, long) - Constructor for class mulan.classifier.neural.MMPLearner

Creates a new instance of MMPLearner.

MMPMaxUpdateRule - Class in mulan.classifier.neural

Implementation of max update rule for MMPLearner.

MMPMaxUpdateRule(List<Neuron>, RankingLossFunction) - Constructor for class mulan.classifier.neural.MMPMaxUpdateRule

Creates a new instance of MMPMaxUpdateRule.

MMPRandomizedUpdateRule - Class in mulan.classifier.neural

Implementation of randomized update rule for MMPLearner.

MMPRandomizedUpdateRule(List<Neuron>, RankingLossFunction) - Constructor for class mulan.classifier.neural.MMPRandomizedUpdateRule

Creates a new instance of MMPRandomizedUpdateRule.

MMPUniformUpdateRule - Class in mulan.classifier.neural

Implementation of uniform update rule for MMPLearner.

MMPUniformUpdateRule(List<Neuron>, RankingLossFunction) - Constructor for class mulan.classifier.neural.MMPUniformUpdateRule

Creates a new instance of MMPUniformUpdateRule.

MMPUpdateRuleBase - Class in mulan.classifier.neural

The base class of update rules for MMPLearner.

MMPUpdateRuleBase(List<Neuron>, RankingLossFunction) - Constructor for class mulan.classifier.neural.MMPUpdateRuleBase

Creates a new instance of MMPUpdateRuleBase.

MMPUpdateRuleType - Enum in mulan.classifier.neural

The enumeration of update rules, which can be used by the MMPLearner to update its model in learning phase.

ModelInitializationException - Exception in mulan.classifier

Exception thrown by the MultiLabelLearner when learner is queried for prediction on data before model is internally built by learning from training data.

ModelInitializationException(String) - Constructor for exception mulan.classifier.ModelInitializationException

Creates a new instance of ModelInitializationException with the specified detail message.

ModelInitializationException(String, Throwable) - Constructor for exception mulan.classifier.ModelInitializationException

Creates a new instance of ModelInitializationException with the specified detail message and nested exception.

ModelUpdateRule - Interface in mulan.classifier.neural

Represents an update rule, which can be used by a learner, to process an input example in learning phase and perform an update of a model when necessary.

modifiedInstanceX(Instance, String) - Method in class mulan.classifier.meta.thresholding.Meta

A method that modify an instance

mulan.classifier - package mulan.classifier

mulan.classifier.lazy - package mulan.classifier.lazy

mulan.classifier.meta - package mulan.classifier.meta

mulan.classifier.meta.thresholding - package mulan.classifier.meta.thresholding

mulan.classifier.neural - package mulan.classifier.neural

mulan.classifier.neural.model - package mulan.classifier.neural.model

mulan.classifier.transformation - package mulan.classifier.transformation

mulan.core - package mulan.core

mulan.data - package mulan.data

mulan.dimensionalityReduction - package mulan.dimensionalityReduction

mulan.evaluation - package mulan.evaluation

mulan.evaluation.loss - package mulan.evaluation.loss

mulan.evaluation.measure - package mulan.evaluation.measure

mulan.experiments - package mulan.experiments

mulan.transformations - package mulan.transformations

mulan.transformations.multiclass - package mulan.transformations.multiclass

MulanException - Exception in mulan.core

Represents a root base class for checked exceptions thrown within Mulan library.

MulanException(String) - Constructor for exception mulan.core.MulanException

Creates a new instance of MulanException with the specified detail message.

MulanException(String, Throwable) - Constructor for exception mulan.core.MulanException

Creates a new instance of MulanException with the specified detail message and nested exception.

MulanJavadoc - Class in mulan.core

This class uses weka's Javadoc auto-generation classes to generate Javadoc
comments and replaces the content between certain comment tags.

MulanJavadoc() - Constructor for class mulan.core.MulanJavadoc

MulanRuntimeException - Exception in mulan.core

Represents a root base class for unchecked exceptions thrown within Mulan library.

MulanRuntimeException(String) - Constructor for exception mulan.core.MulanRuntimeException

Creates a new instance of MulanRuntimeException with the specified detail message.

MulanRuntimeException(String, Throwable) - Constructor for exception mulan.core.MulanRuntimeException

Creates a new instance of MulanRuntimeException with the specified detail message and nested exception.

MultiClassAttributeEvaluator - Class in mulan.dimensionalityReduction

Performs attribute evaluation using single-label transformations.

MultiClassAttributeEvaluator(ASEvaluation, MultiClassTransformation, MultiLabelInstances) - Constructor for class mulan.dimensionalityReduction.MultiClassAttributeEvaluator

Constructor that uses an evaluator on a multi-label dataset using a transformation

MultiClassLearner - Class in mulan.classifier.transformation

MultiClassLearner(Classifier, MultiClassTransformation) - Constructor for class mulan.classifier.transformation.MultiClassLearner

Initializes learner

MultiClassTransformation - Interface in mulan.transformations.multiclass

The interface for single-label multi-class transformations.

MultiClassTransformationBase - Class in mulan.transformations.multiclass

The base class for multi-class transformation methods.

MultiClassTransformationBase() - Constructor for class mulan.transformations.multiclass.MultiClassTransformationBase

MultiLabelInstances - Class in mulan.data

Implements multi-label instances data set.

MultiLabelInstances(String, int) - Constructor for class mulan.data.MultiLabelInstances

Creates a new instance of MultiLabelInstances data.

MultiLabelInstances(InputStream, int) - Constructor for class mulan.data.MultiLabelInstances

Creates a new instance of MultiLabelInstances data from the supplied InputStream data source.

MultiLabelInstances(Instances, String) - Constructor for class mulan.data.MultiLabelInstances

Creates a new instance of MultiLabelInstances data.

MultiLabelInstances(String, String) - Constructor for class mulan.data.MultiLabelInstances

Creates a new instance of MultiLabelInstances data.

MultiLabelInstances(InputStream, InputStream) - Constructor for class mulan.data.MultiLabelInstances

Creates a new instance of MultiLabelInstances data from the supplied InputStream data source.

MultiLabelInstances(Instances, LabelsMetaData) - Constructor for class mulan.data.MultiLabelInstances

Creates a new instance of MultiLabelInstances data from existing Instances and LabelsMetaData.

MultiLabelKNN - Class in mulan.classifier.lazy

Superclass of all KNN based multi-label algorithms

MultiLabelKNN() - Constructor for class mulan.classifier.lazy.MultiLabelKNN

The default constructor

MultiLabelKNN(int) - Constructor for class mulan.classifier.lazy.MultiLabelKNN

Initializes the number of neighbors

MultiLabelLearner - Interface in mulan.classifier

Common root interface for all multi-label learner types.

MultiLabelLearnerBase - Class in mulan.classifier

Common root base class for all multi-label learner types.

MultiLabelLearnerBase() - Constructor for class mulan.classifier.MultiLabelLearnerBase

MultiLabelLossFunction - Interface in mulan.evaluation.loss

Interfance for loss functions

MultiLabelMetaLearner - Class in mulan.classifier.meta

Base class for multi-label learners, which use other multi-label learners

MultiLabelMetaLearner(MultiLabelLearner) - Constructor for class mulan.classifier.meta.MultiLabelMetaLearner

Creates a new instance.

MultiLabelOutput - Class in mulan.classifier

Class representing the output of a MultiLabelLearner.

MultiLabelOutput(boolean[]) - Constructor for class mulan.classifier.MultiLabelOutput

Creates a new instance of MultiLabelOutput.

MultiLabelOutput(int[]) - Constructor for class mulan.classifier.MultiLabelOutput

Creates a new instance of MultiLabelOutput.

MultiLabelOutput(double[], double) - Constructor for class mulan.classifier.MultiLabelOutput

Creates a new instance of MultiLabelOutput.

MultiLabelOutput(double[]) - Constructor for class mulan.classifier.MultiLabelOutput

Creates a new instance of MultiLabelOutput.

MultiLabelOutput(boolean[], double[]) - Constructor for class mulan.classifier.MultiLabelOutput

Creates a new instance of MultiLabelOutput.

MultiLabelStacking - Class in mulan.classifier.transformation

This class is an implementation of the (BR)^2 or Multi-Label stacking method.

MultiLabelStacking() - Constructor for class mulan.classifier.transformation.MultiLabelStacking

An empty constructor

MultiLabelStacking(Classifier, Classifier) - Constructor for class mulan.classifier.transformation.MultiLabelStacking

A constructor with 2 arguments

MultipleEvaluation - Class in mulan.evaluation

Simple class that includes an array, whose elements are lists of evaluation evaluations.

MultipleEvaluation(MultiLabelInstances) - Constructor for class mulan.evaluation.MultipleEvaluation

Constructs a new object

MultipleEvaluation(Evaluation[], MultiLabelInstances) - Constructor for class mulan.evaluation.MultipleEvaluation

Constructs a new object with given array of evaluations

N

NeuralNet - Interface in mulan.classifier.neural.model

Common interface for interaction with a neural network representation.

Neuron - Class in mulan.classifier.neural.model

Implementation of a neuron unit.

Neuron(ActivationFunction, int, double) - Constructor for class mulan.classifier.neural.model.Neuron

Creates a new Neuron instance.

Neuron(ActivationFunction, int, double, Random) - Constructor for class mulan.classifier.neural.model.Neuron

Creates a new Neuron instance.

Neuron(ActivationFunction, int, double, Collection<Neuron>) - Constructor for class mulan.classifier.neural.model.Neuron

Creates a new Neuron instance.

nodata - Variable in class mulan.classifier.transformation.CalibratedLabelRanking

whether no data exist for one-vs-one learning

norm(double[]) - Static method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

calculates the norm of a vector

NormalizationFilter - Class in mulan.classifier.neural

Performs a normalization of numeric attributes of the data set.

NormalizationFilter(MultiLabelInstances, boolean, double, double) - Constructor for class mulan.classifier.neural.NormalizationFilter

Creates a new instance of NormalizationFilter class for given data set.

NormalizationFilter(MultiLabelInstances, boolean) - Constructor for class mulan.classifier.neural.NormalizationFilter

Creates a new instance of NormalizationFilter class for given data set.

normalize(Instance) - Method in class mulan.classifier.neural.NormalizationFilter

Performs a normalization of numerical attributes on given instance.

normalize(double[]) - Static method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

normalizes an array (in the range of [0,1])

numberOfClusters() - Method in class mulan.classifier.meta.ConstrainedKMeans

Returns the number of clusters.

numClustersTipText() - Method in class mulan.classifier.meta.ConstrainedKMeans

Returns the tip text for this property

numLabels - Variable in class mulan.classifier.MultiLabelLearnerBase

The number of labels the learner can handle.

numModels - Variable in class mulan.classifier.transformation.CalibratedLabelRanking

number of one vs one models

numOfLabels - Variable in class mulan.evaluation.measure.LabelBasedAUC

The number of labels

numOfLabels - Variable in class mulan.evaluation.measure.LabelBasedAveragePrecision

the number of labels

numOfLabels - Variable in class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

the number of labels

numOfLabels - Variable in class mulan.transformations.multiclass.MultiClassTransformationBase

the number of labels

numOfModels - Variable in class mulan.classifier.transformation.EnsembleOfClassifierChains

The number of classifier chain models

numOfModels - Variable in class mulan.classifier.transformation.EnsembleOfPrunedSets

Parameter for the number of models that constitute the ensemble

numOfNeighbors - Variable in class mulan.classifier.lazy.MultiLabelKNN

Number of neighbors used in the k-nearest neighbor algorithm

O

OneError - Class in mulan.evaluation.loss

Implementation of the one-error loss function.

OneError() - Constructor for class mulan.evaluation.loss.OneError

OneError - Class in mulan.evaluation.measure

Measure based on the one error loss function

OneError() - Constructor for class mulan.evaluation.measure.OneError

Creates an instance of this class based on the corresponding loss function

OneMinusAveragePrecision - Class in mulan.evaluation.loss

Implementation of the average precision as loss function.

OneMinusAveragePrecision() - Constructor for class mulan.evaluation.loss.OneMinusAveragePrecision

OneThreshold - Class in mulan.classifier.meta.thresholding

Class that estimates a single threshold for all labels and examples.

OneThreshold() - Constructor for class mulan.classifier.meta.thresholding.OneThreshold

Default constructor

OneThreshold(MultiLabelLearner, BipartitionMeasureBase, int) - Constructor for class mulan.classifier.meta.thresholding.OneThreshold

OneThreshold(MultiLabelLearner, BipartitionMeasureBase) - Constructor for class mulan.classifier.meta.thresholding.OneThreshold

oneVsOneModels - Variable in class mulan.classifier.transformation.CalibratedLabelRanking

array holding the one vs one models

order(int[]) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

orders the ranking scores according to their attributes' original indices

P

p - Variable in class mulan.classifier.transformation.LabelsetPruning

parameter for the threshold of number of occurences of a labelset

partitionToString(int[][]) - Static method in class mulan.classifier.meta.EnsembleOfSubsetLearners

Returns a string representation of the labels partition.

partitionToString(int[][]) - Static method in class mulan.data.GreedyLabelClustering

Returns a string representation of the labels partition.

PatternRecognition07MLkNN - Class in mulan.experiments

Class replicating an experiment from a published paper

PatternRecognition07MLkNN() - Constructor for class mulan.experiments.PatternRecognition07MLkNN

percentage - Variable in class mulan.classifier.transformation.EnsembleOfPrunedSets

Percentage of data

PPT - Class in mulan.classifier.transformation

Class implementing the Pruned Problem Transformation algorithm (PPT) .

PPT() - Constructor for class mulan.classifier.transformation.PPT

Default constructor

PPT(Classifier, int, PPT.Strategy) - Constructor for class mulan.classifier.transformation.PPT

Constructor that initializes learner with base algorithm, parameter p and strategy for processing infrequent labelsets

PPT.Strategy - Enum in mulan.classifier.transformation

strategies for processing infrequent labelsets

precision(double, double, double) - Static method in class mulan.evaluation.measure.InformationRetrievalMeasures

Computation of precision based on tp, fp and fn.

prepareClassifierInstances(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.Meta

Prepares the instances for the predictor of labels/threshold

printPhiCorrelations() - Method in class mulan.data.Statistics

Prints out phi correlations

printPhiDiagram(double) - Method in class mulan.data.Statistics

This method prints data, useful for the visualization of Phi per dataset.

priors() - Method in class mulan.data.Statistics

returns the prior probabilities of the labels

process(DataPair, Map<String, Object>) - Method in class mulan.classifier.neural.MMPUpdateRuleBase

process(DataPair, Map<String, Object>) - Method in interface mulan.classifier.neural.ModelUpdateRule

Process the training example and performs a model update when suitable.

processInput(double[]) - Method in class mulan.classifier.neural.model.Neuron

Process an input pattern vector and returns the response of the Neuron.

PrunedSets - Class in mulan.classifier.transformation

Class implementing the Pruned Sets algorithm (PS).

PrunedSets() - Constructor for class mulan.classifier.transformation.PrunedSets

Default constructor

PrunedSets(Classifier, int, PrunedSets.Strategy, int) - Constructor for class mulan.classifier.transformation.PrunedSets

Constructor that initializes learner with base algorithm, parameter p and strategy for processing infrequent labelsets

PrunedSets.Strategy - Enum in mulan.classifier.transformation

strategies for processing infrequent labelsets

R

RAkEL - Class in mulan.classifier.meta

Class implementing a generalized version of the RAkEL (RAndom k-labELsets) algorithm.

RAkEL() - Constructor for class mulan.classifier.meta.RAkEL

Default constructor

RAkEL(MultiLabelLearner) - Constructor for class mulan.classifier.meta.RAkEL

Creates an instance based on a given multi-label learner

RAkEL(MultiLabelLearner, int, int) - Constructor for class mulan.classifier.meta.RAkEL

Creates an instance given a specific multi-label learner, number of models and size of subsets

RAkEL(MultiLabelLearner, int, int, double) - Constructor for class mulan.classifier.meta.RAkEL

Creates an instance given a specific multi-label learner, number of models, size of subsets and threshold

RAkELd - Class in mulan.classifier.meta

Class implementing a generalized version of the RAkEL-d (RAndom k-labELsets) algorithm with disjoint labelsets.

RAkELd() - Constructor for class mulan.classifier.meta.RAkELd

Default constructor

RAkELd(MultiLabelLearner) - Constructor for class mulan.classifier.meta.RAkELd

Construct a new instance based on the given multi-label learner

RAkELd(MultiLabelLearner, int) - Constructor for class mulan.classifier.meta.RAkELd

Constructs a new instance based on the given multi-label learner and size of subset

rand - Variable in class mulan.classifier.transformation.EnsembleOfClassifierChains

Random number generator

rand - Variable in class mulan.classifier.transformation.EnsembleOfPrunedSets

Random number generator

Rand - Variable in class mulan.classifier.transformation.LabelPowerset

Random number generator for randomly solving tied predictions

RandomIndexOfMax(double[], Random) - Static method in class mulan.core.Util

Procedure to find index of maximum value in the specified array.

rankAsc(double[]) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

returns a ranking of attributes (where each attribute is represented by its index)

rankDesc(double[]) - Method in class mulan.dimensionalityReduction.BinaryRelevanceAttributeEvaluator

returns a ranking of attributes (where each attribute is represented by its index)

Ranker - Class in mulan.dimensionalityReduction

Ranks attributes according to an AttributeEvaluator.

Ranker() - Constructor for class mulan.dimensionalityReduction.Ranker

RankingLoss - Class in mulan.evaluation.loss

Implementation of the "ranking loss" ranking loss function.

RankingLoss() - Constructor for class mulan.evaluation.loss.RankingLoss

RankingLoss - Class in mulan.evaluation.measure

Implementation of the ranking loss measure.

RankingLoss() - Constructor for class mulan.evaluation.measure.RankingLoss

Creates an instance of this class based on the corresponding loss function

RankingLossFunction - Interface in mulan.evaluation.loss

Interfance for ranking loss functions

RankingLossFunctionBase - Class in mulan.evaluation.loss

Base class for ranking loss functions

RankingLossFunctionBase() - Constructor for class mulan.evaluation.loss.RankingLossFunctionBase

RankingMeasureBase - Class in mulan.evaluation.measure

RankingMeasureBase() - Constructor for class mulan.evaluation.measure.RankingMeasureBase

ranksFromValues(double[]) - Static method in class mulan.classifier.MultiLabelOutput

Creates a ranking form specified values/confidences.

RCut - Class in mulan.classifier.meta.thresholding

Classs that implements RCut(Rank-based cut).

RCut() - Constructor for class mulan.classifier.meta.thresholding.RCut

Default constructor

RCut(MultiLabelLearner) - Constructor for class mulan.classifier.meta.thresholding.RCut

Creates a new instance of RCut

RCut(MultiLabelLearner, BipartitionMeasureBase, int) - Constructor for class mulan.classifier.meta.thresholding.RCut

Creates a new instance of RCut

RCut(MultiLabelLearner, BipartitionMeasureBase) - Constructor for class mulan.classifier.meta.thresholding.RCut

Creates a new instance of RCut

readExternal(ObjectInput) - Method in class mulan.data.LabelsMetaDataImpl

recall(double, double, double) - Static method in class mulan.evaluation.measure.InformationRetrievalMeasures

Computation of recall based on tp, fp and fn.

recursiveTraversal(File) - Static method in class mulan.core.MulanJavadoc

Recursively visit all files

reintegrateModifiedDataSet(Instances) - Method in class mulan.data.MultiLabelInstances

If Instances data set are retrieved from MultiLabelInstances and post-processed, modified by custom code, it can be again reintegrated into MultiLabelInstances if needed.

RemoveAllLabels - Class in mulan.transformations

This transformation removes all the label attributes from a multi-label dataset

RemoveAllLabels() - Constructor for class mulan.transformations.RemoveAllLabels

removeChildNode(LabelNode) - Method in class mulan.data.LabelNodeImpl

Removes the specified LabelNode from the set of child nodes.

removeLabelNode(String) - Method in class mulan.data.LabelsMetaDataImpl

Removes LabelNode specified by the name.

removeNeuron(Neuron) - Method in class mulan.classifier.neural.model.Neuron

Removes a connection to a specified Neuron.

reset() - Method in class mulan.classifier.neural.model.BasicNeuralNet

reset() - Method in interface mulan.classifier.neural.model.NeuralNet

Perform reset, re-initialization of neural network.

reset() - Method in class mulan.classifier.neural.model.Neuron

Performs reset, re-initialization of the Neuron.

reset() - Method in class mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase

reset() - Method in class mulan.evaluation.measure.LabelBasedAUC

reset() - Method in class mulan.evaluation.measure.LabelBasedAveragePrecision

reset() - Method in class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

reset() - Method in interface mulan.evaluation.measure.Measure

Resets the cumulated measure value, so the process of computation can be started from beginning (e.g. for a new series of outputs from learning task).

reset() - Method in class mulan.evaluation.measure.RankingMeasureBase

resetRandomSeed(Object) - Method in class mulan.classifier.meta.SubsetLearner

Invokes the setSeed(1) or setRandomSeed(1) method of the supplied object's Class, if such method exist.

resetSubsets(int[][]) - Method in class mulan.classifier.meta.SubsetLearner

Reset the label set partitioning.

S

samplingPercentage - Variable in class mulan.classifier.transformation.EnsembleOfClassifierChains

The size of each sample, as a percentage of the training size Used when useSamplingWithReplacement is false

saveObject(String) - Method in class mulan.classifier.transformation.MultiLabelStacking

Saves a MultiLabelStacking object in a file

SCut - Class in mulan.classifier.meta.thresholding

Class that implements the SCut method (Score-based local optimization).

SCut() - Constructor for class mulan.classifier.meta.thresholding.SCut

Default constructor

SCut(MultiLabelLearner, BipartitionMeasureBase, int) - Constructor for class mulan.classifier.meta.thresholding.SCut

Constructor that initializes the learner with a base algorithm , Measure and num of folds

SCut(MultiLabelLearner, BipartitionMeasureBase) - Constructor for class mulan.classifier.meta.thresholding.SCut

Creates a new instance of SCut

search(AttributeEvaluator, MultiLabelInstances) - Method in class mulan.dimensionalityReduction.Ranker

Calls a specified AttributeEvaluator to evaluate each feature attribute of specified MultiLabelInstances data set, excluding labels.

seed - Variable in class mulan.data.ConditionalDependenceIdentifier

Seed for replication of random experiments

SelectBasedOnFrequency - Class in mulan.transformations.multiclass

Class that implement the Select-Max and Select-Min transformation methods.

SelectBasedOnFrequency(SelectionType) - Constructor for class mulan.transformations.multiclass.SelectBasedOnFrequency

Initializes the transformation with a SelectionType

SelectionType - Enum in mulan.transformations.multiclass

SelectRandom - Class in mulan.transformations.multiclass

Class that implement the Select-Random transformation method

SelectRandom() - Constructor for class mulan.transformations.multiclass.SelectRandom

setAllowedNonImprovementSteps(int) - Method in class mulan.data.GreedyLabelClustering

setBagSizePercent(int) - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

Sets the size of each bag sample, as a percentage of the training size

setConfidenceCalculationMethod(int) - Method in class mulan.classifier.transformation.LabelPowerset

Sets the method of calculating probabilities for each label

setConvertNominalToBinary(boolean) - Method in class mulan.classifier.neural.MMPLearner

Sets whether nominal attributes from input data set has to be converted to binary prior to learning (and respectively making a prediction).

setCriticalValue(double) - Method in class mulan.data.ConditionalDependenceIdentifier

setCriticalValue(double) - Method in class mulan.data.GreedyLabelClustering

setCriticalValue(double) - Method in class mulan.data.UnconditionalChiSquareIdentifier

setCvMaxK(int) - Method in class mulan.classifier.lazy.BRkNN

set the maximum number of neighbors to be evaluated via cross-validation

setDebug(boolean) - Method in class mulan.classifier.meta.thresholding.RCut

setDebug(boolean) - Method in interface mulan.classifier.MultiLabelLearner

Sets whether debugging information should be output by the model

setDebug(boolean) - Method in class mulan.classifier.MultiLabelLearnerBase

Set debugging mode.

setDependenceIdentifier(LabelPairsDependenceIdentifier) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setDfunc(DistanceFunction) - Method in class mulan.classifier.lazy.MultiLabelKNN

Sets a distance function

setDir(String) - Static method in class mulan.core.MulanJavadoc

Sets the direcrory

setDistance(double) - Method in class mulan.classifier.meta.ConstrainedKMeans.bucketInstance

setDistances(double[]) - Method in class mulan.classifier.meta.ConstrainedKMeans.bucketInstance

Sets the distances to other instances

setDistanceWeighting(int) - Method in class mulan.classifier.lazy.MultiLabelKNN

setDynamicDiversityThreshold(double) - Static method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setError(double) - Method in class mulan.classifier.neural.model.Neuron

Sets the error term of the Neuron.

setEval(ASEvaluation) - Method in class mulan.classifier.transformation.MultiLabelStacking

Sets the attribute selection evaluation class

setFolds(int) - Method in class mulan.classifier.meta.thresholding.MetaLabeler

Sets the number of folds for internal cv

setHiddenLayers(int[]) - Method in class mulan.classifier.neural.BPMLL

Sets the topology of hidden layers for neural network.

setIncludeAttrs(boolean) - Method in class mulan.classifier.transformation.MultiLabelStacking

Sets the value of includeAttrs

setInternalSubsetLearnerDebug(boolean) - Method in class mulan.data.GreedyLabelClustering

setkSelectionViaCV(boolean) - Method in class mulan.classifier.lazy.BRkNN

setLearningRate(double) - Method in class mulan.classifier.neural.BPMLL

Sets the learning rate.

setMakePredictionsBasedOnConfidences(boolean) - Method in class mulan.classifier.transformation.LabelPowerset

Sets a threshold for obtaining the bipartition

setMaxIterations(int) - Method in class mulan.classifier.meta.ConstrainedKMeans

setMeasure(Measure) - Method in class mulan.data.GreedyLabelClustering

setMetaAlgorithm(Classifier) - Method in class mulan.classifier.transformation.MultiLabelStacking

Sets the type of the meta classifier and initializes the ensemble

setMetaPercentage(double) - Method in class mulan.classifier.transformation.MultiLabelStacking

Sets the value of metaPercentage

setNormalize(boolean) - Method in class mulan.classifier.transformation.MultiLabelStacking

Sets the value of normalize

setNormalizeAttributes(boolean) - Method in class mulan.classifier.neural.BPMLL

Sets whether attributes of instances data (except label attributes) should be normalized prior to building the learner.

setNumClusters(int) - Method in class mulan.classifier.meta.ConstrainedKMeans

set the number of clusters to generate

setNumFolds(int) - Method in class mulan.data.ConditionalDependenceIdentifier

setNumFolds(int) - Method in class mulan.data.GreedyLabelClustering

setNumModels(int) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setNumModels(int) - Method in class mulan.classifier.meta.RAkEL

Sets the number of models

setNumOfPartitionsForDiversity(int) - Static method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setNumOfRandomPartitions(int) - Static method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setPair(int[]) - Method in class mulan.data.LabelsPair

setParent(LabelNode) - Method in class mulan.data.LabelNodeImpl

Sets a node as the parent of this node

setPartialBuild(boolean) - Method in class mulan.classifier.transformation.MultiLabelStacking

sets the value for partialBuild

setRnd(Random) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setSamplingPercentage(double) - Method in class mulan.classifier.transformation.EnsembleOfClassifierChains

Sets the sampling percentage

setScore(Double) - Method in class mulan.data.LabelsPair

setSeed(int) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setSeed(int) - Method in class mulan.classifier.meta.RAkEL

Sets the seed for random number generation

setSeed(int) - Method in class mulan.classifier.meta.RAkELd

Sets the seed for random number generation

setSeed() - Method in class mulan.classifier.meta.SubsetLearner

Set random seed of all internal Learners to 1.

setSeed(int) - Method in class mulan.classifier.transformation.LabelPowerset

Setting a seed for random selection in case of ties during prediction

setSeed(int) - Method in class mulan.data.ConditionalDependenceIdentifier

setSeed(int) - Method in class mulan.evaluation.Evaluator

Sets the seed for reproduction of cross-validation results

setSelectDiverseModels(boolean) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setSizeOfSubset(int) - Method in class mulan.classifier.meta.RAkEL

Sets the size of the subsets

setSizeOfSubset(int) - Method in class mulan.classifier.meta.RAkELd

Sets the size of the subsets

setStandardVoting(boolean) - Method in class mulan.classifier.transformation.CalibratedLabelRanking

Set Prediction to standard voting mode.

setThreshold(double) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setThreshold(double) - Method in class mulan.classifier.transformation.LabelPowerset

The threshold for obtaining the bipartition from probabilities

setTrainingEpochs(int) - Method in class mulan.classifier.neural.BPMLL

Sets the number of training epochs.

setTrainingEpochs(int) - Method in class mulan.classifier.neural.MMPLearner

Sets the number of training epochs.

setUseCache(boolean) - Method in class mulan.classifier.meta.SubsetLearner

Sets whether cache mechanism will be used

setUseSubsetLearnerCache(boolean) - Method in class mulan.classifier.meta.EnsembleOfSubsetLearners

setUseSubsetLearnerCache(boolean) - Method in class mulan.data.GreedyLabelClustering

setWeightsDecayRegularization(double) - Method in class mulan.classifier.neural.BPMLL

Sets the regularization cost term for weights decay.

size() - Method in class mulan.data.LabelSet

The number of set members.

smooth - Variable in class mulan.classifier.lazy.MLkNN

Smoothing parameter controlling the strength of uniform prior
(Default value is set to 1 which yields the Laplace smoothing).

specificity(double, double, double) - Static method in class mulan.evaluation.measure.InformationRetrievalMeasures

Computation of specificity based on tn, fp and fn.

Statistics - Class in mulan.data

Class for calculating statistics of a multi-label dataset.

Statistics() - Constructor for class mulan.data.Statistics

Stratification - Interface in mulan.data

Interface for multi-label stratification methods

stratify(MultiLabelInstances, int) - Method in class mulan.data.IterativeStratification

stratify(MultiLabelInstances, int) - Method in class mulan.data.LabelPowersetStratification

stratify(MultiLabelInstances, int) - Method in interface mulan.data.Stratification

Creates a number of folds via stratified sampling

SubsetAccuracy - Class in mulan.evaluation.measure

Implementation of the subset accuracy measure.

SubsetAccuracy() - Constructor for class mulan.evaluation.measure.SubsetAccuracy

SubsetLearner - Class in mulan.classifier.meta

A class for learning a classifier according to disjoint label subsets: a multi-label learner (the Label Powerset by default) is applied to subsets with multiple labels and a single-label learner is applied to single label subsets.

SubsetLearner() - Constructor for class mulan.classifier.meta.SubsetLearner

Default constructor

SubsetLearner(int[][], Classifier) - Constructor for class mulan.classifier.meta.SubsetLearner

Initialize the SubsetLearner with labels subsets partitioning and single label learner.

SubsetLearner(int[][], MultiLabelLearner, Classifier) - Constructor for class mulan.classifier.meta.SubsetLearner

Initialize the SubsetLearner with labels set partitioning, multilabel and single label learners.

SubsetLearner(LabelClustering, MultiLabelLearner, Classifier) - Constructor for class mulan.classifier.meta.SubsetLearner

Initialize the SubsetLearner with a label clustering method, multilabel and single label learners.

sum - Variable in class mulan.evaluation.measure.ExampleBasedBipartitionMeasureBase

The current sum of the measure

sum - Variable in class mulan.evaluation.measure.RankingMeasureBase

The current sum of the measure

T

threshold - Variable in class mulan.classifier.transformation.EnsembleOfPrunedSets

Parameter for the threshold of discretization of prediction output

threshold - Variable in class mulan.classifier.transformation.LabelPowerset

Threshold used for deciding the 1/0 output value of each label based on the corresponding confidences as calculated by the method introduced in the PPT algorithm

ThresholdFunction - Class in mulan.classifier.neural

Implementation of a threshold function.

ThresholdFunction(double[][], double[][]) - Constructor for class mulan.classifier.neural.ThresholdFunction

Creates a new instance of ThresholdFunction and builds the function based on input parameters.

ThresholdPrediction - Class in mulan.classifier.meta.thresholding

Class that learns to predict a different threshold per exampleFor more information, see

Elisseeff, Andre, Weston, Jason: A kernel method for multi-labelled classification.

ThresholdPrediction() - Constructor for class mulan.classifier.meta.thresholding.ThresholdPrediction

Default constructor

ThresholdPrediction(MultiLabelLearner, Classifier, String, int) - Constructor for class mulan.classifier.meta.thresholding.ThresholdPrediction

Constructor that initializes the learner

toBitString() - Method in class mulan.data.LabelSet

Constructs a bitstring from the current labelset.

toBooleanArray() - Method in class mulan.data.LabelSet

Get an array representation of this set.

toCSV() - Method in class mulan.evaluation.Evaluation

Returns a CSV representation of the calculated measures

toCSV() - Method in class mulan.evaluation.MultipleEvaluation

Returns a CSV string representation of the results

toDoubleArray() - Method in class mulan.data.LabelSet

Get an array representation of this set.

topPhiCorrelatedLabels(int, int) - Method in class mulan.data.Statistics

Returns the indices of the labels that have the strongest phi correlation with the label which is given as a parameter.

toString() - Method in class mulan.classifier.meta.ConstrainedKMeans

return a string describing this clusterer

toString() - Method in class mulan.classifier.MultiLabelOutput

toString() - Method in class mulan.data.LabelSet

A comma-separated list of label names enclosed in curlies.

toString() - Method in class mulan.data.LabelsPair

toString() - Method in class mulan.data.Statistics

returns various multilabel statistics in textual representation

toString() - Method in class mulan.evaluation.Evaluation

Returns a string with the results of the evaluation

toString() - Method in class mulan.evaluation.measure.MeasureBase

Returns a string with the value of a measure

toString() - Method in class mulan.evaluation.MultipleEvaluation

Returns a string with the results of the evaluation

train - Variable in class mulan.classifier.lazy.MultiLabelKNN

The training instances

train - Variable in class mulan.classifier.transformation.MultiLabelStacking

the training instances

trainingdata - Variable in class mulan.classifier.transformation.CalibratedLabelRanking

temporary training data for each one vs one model

transformation - Variable in class mulan.classifier.transformation.LabelPowerset

The object that performs the data transformation

TransformationBasedMultiLabelLearner - Class in mulan.classifier.transformation

TransformationBasedMultiLabelLearner() - Constructor for class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

Creates a new instance of TransformationBasedMultiLabelLearner with default J48 base classifier.

TransformationBasedMultiLabelLearner(Classifier) - Constructor for class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

Creates a new instance.

transformData(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.Meta

abstract method that transforms the training data to meta data

transformData(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.MetaLabeler

transformData(MultiLabelInstances) - Method in class mulan.classifier.meta.thresholding.ThresholdPrediction

transformed - Variable in class mulan.classifier.transformation.IncludeLabelsClassifier

A dataset with the format needed by the base classifier.

transformInstance(Instance, int) - Method in class mulan.transformations.BinaryRelevanceTransformation

Remove all label attributes except labelToKeep

transformInstance(Instance, int[], int) - Static method in class mulan.transformations.BinaryRelevanceTransformation

Remove all label attributes except label at position indexToKeep

transformInstance(Instance) - Method in class mulan.transformations.IncludeLabelsTransformation

Transform an unlabeled instance to the format expected by the binary classifier

transformInstance(Instance, int[]) - Method in class mulan.transformations.LabelPowersetTransformation

transformInstance(Instance, int[]) - Static method in class mulan.transformations.RemoveAllLabels

transformInstances(int) - Method in class mulan.transformations.BinaryRelevanceTransformation

Remove all label attributes except labelToKeep

transformInstances(Instances, int[], int) - Static method in class mulan.transformations.BinaryRelevanceTransformation

Remove all label attributes except that at indexOfLabelToKeep

transformInstances(MultiLabelInstances) - Method in class mulan.transformations.IncludeLabelsTransformation

transformInstances(MultiLabelInstances) - Method in class mulan.transformations.LabelPowersetTransformation

transformInstances(MultiLabelInstances) - Method in interface mulan.transformations.multiclass.MultiClassTransformation

Transforms a multi-label dataset to a multi-class single label dataset

transformInstances(MultiLabelInstances) - Method in class mulan.transformations.multiclass.MultiClassTransformationBase

transformInstances(MultiLabelInstances) - Method in class mulan.transformations.multiclass.SelectBasedOnFrequency

transformInstances(MultiLabelInstances) - Static method in class mulan.transformations.RemoveAllLabels

transformInstances(Instances, int[]) - Static method in class mulan.transformations.RemoveAllLabels

Removes the labels from a set of instances

traverse() - Static method in class mulan.core.MulanJavadoc

Recursively visit all files

trueNegatives - Variable in class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

the number of true negatives for each label

truePositives - Variable in class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

the number of true positives for each label

U

UnconditionalChiSquareIdentifier - Class in mulan.data

A class for identification of unconditional dependence between each pair of labels using Chi Square Test For Independence.

UnconditionalChiSquareIdentifier() - Constructor for class mulan.data.UnconditionalChiSquareIdentifier

uncorrelatedLabels(int, double) - Method in class mulan.data.Statistics

returns the indices of the labels whose phi coefficient values lie between -bound <= phi <= bound

update(MultiLabelOutput, boolean[]) - Method in interface mulan.evaluation.measure.Measure

Computes the value of a measure for the given prediction and true labels.

update(MultiLabelOutput, boolean[]) - Method in class mulan.evaluation.measure.MeasureBase

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.BipartitionMeasureBase

Updates the measure based on an example

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.ExampleBasedAccuracy

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.ExampleBasedFMeasure

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.ExampleBasedPrecision

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.ExampleBasedRecall

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.ExampleBasedSpecificity

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.LabelBasedBipartitionMeasureBase

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.LossBasedBipartitionMeasureBase

updateBipartition(boolean[], boolean[]) - Method in class mulan.evaluation.measure.SubsetAccuracy

updateClassifier(MultiLabelInstances, int) - Method in class mulan.classifier.meta.RAkELd

Updates the current ensemble by training a specific classifier

updateConfidence(double[], boolean[]) - Method in class mulan.evaluation.measure.ConfidenceMeasureBase

Updates the measure for a new example

updateConfidence(double[], boolean[]) - Method in class mulan.evaluation.measure.LabelBasedAUC

updateConfidence(double[], boolean[]) - Method in class mulan.evaluation.measure.LabelBasedAveragePrecision

updateInternal(MultiLabelOutput, boolean[]) - Method in class mulan.evaluation.measure.BipartitionMeasureBase

updateInternal(MultiLabelOutput, boolean[]) - Method in class mulan.evaluation.measure.ConfidenceMeasureBase

updateInternal(MultiLabelOutput, boolean[]) - Method in class mulan.evaluation.measure.MeasureBase

Updates the measure based on an example

updateInternal(MultiLabelOutput, boolean[]) - Method in class mulan.evaluation.measure.RankingMeasureBase

updateJavadoc(String) - Static method in class mulan.core.MulanJavadoc

Updates comments

updateRanking(int[], boolean[]) - Method in class mulan.evaluation.measure.AveragePrecision

updateRanking(int[], boolean[]) - Method in class mulan.evaluation.measure.Coverage

updateRanking(int[], boolean[]) - Method in class mulan.evaluation.measure.LossBasedRankingMeasureBase

updateRanking(int[], boolean[]) - Method in class mulan.evaluation.measure.RankingMeasureBase

Updates the measure based on an example

useConfidences - Variable in class mulan.classifier.transformation.EnsembleOfClassifierChains

Whether the output is computed based on the average votes or on the average confidences

useSamplingWithReplacement - Variable in class mulan.classifier.transformation.EnsembleOfClassifierChains

Whether to use sampling with replacement to create the data of the models of the ensemble

Util - Class in mulan.core

Class which provides various utility methods.

Util() - Constructor for class mulan.core.Util

V

valueOf(String) - Static method in enum mulan.classifier.lazy.BRkNN.ExtensionType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum mulan.classifier.meta.HierarchyBuilder.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum mulan.classifier.neural.MMPUpdateRuleType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum mulan.classifier.transformation.PPT.Strategy

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum mulan.classifier.transformation.PrunedSets.Strategy

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum mulan.transformations.multiclass.SelectionType

Returns the enum constant of this type with the specified name.

values() - Static method in enum mulan.classifier.lazy.BRkNN.ExtensionType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum mulan.classifier.meta.HierarchyBuilder.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum mulan.classifier.neural.MMPUpdateRuleType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum mulan.classifier.transformation.PPT.Strategy

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum mulan.classifier.transformation.PrunedSets.Strategy

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum mulan.transformations.multiclass.SelectionType

Returns an array containing the constants of this enum type, in the order they are declared.

valuesX(MultiLabelLearner, Instance, double[], String) - Method in class mulan.classifier.meta.thresholding.Meta

A method that fill the array "newValues"

virtualLabelModels - Variable in class mulan.classifier.transformation.CalibratedLabelRanking

binary relevance models for the virtual label

W

WEIGHT_INVERSE - Static variable in class mulan.classifier.lazy.MultiLabelKNN

weight by 1/distance.

WEIGHT_NONE - Static variable in class mulan.classifier.lazy.MultiLabelKNN

no weighting.

WEIGHT_SIMILARITY - Static variable in class mulan.classifier.lazy.MultiLabelKNN

weight by 1-distance.

WekaException - Exception in mulan.core

The convenience exception, which can be used to wrap up checked general Exception commonly thrown by underlying Weka library into anonymous runtime exception.

WekaException(String) - Constructor for exception mulan.core.WekaException

Creates a new instance of WekaException with detail mesage.

WekaException(String, Throwable) - Constructor for exception mulan.core.WekaException

Creates a new instance of WekaException with detail message and nested exception.

writeExternal(ObjectOutput) - Method in class mulan.data.LabelsMetaDataImpl

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