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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