mulan.classifier.transformation
Class EnsembleOfPrunedSets

java.lang.Object
  mulan.classifier.MultiLabelLearnerBase
      mulan.classifier.transformation.TransformationBasedMultiLabelLearner
          mulan.classifier.transformation.EnsembleOfPrunedSets

All Implemented Interfaces:

Serializable, MultiLabelLearner, TechnicalInformationHandler

public class EnsembleOfPrunedSets
extends TransformationBasedMultiLabelLearner

Class implementing the Ensemble of Pruned Sets algorithm(EPS) . For more information, see

Read, Jesse, Pfahringer, Bernhard, Holmes, Geoff: Multi-label Classification using Ensembles of Pruned Sets. In: ICDM'08: Eighth IEEE International Conference on Data Mining, 995-1000, 2008.

BibTeX:

 @conference{Read2008,
    author = {Read, Jesse and Pfahringer, Bernhard and Holmes, Geoff},
    booktitle = {ICDM'08: Eighth IEEE International Conference on Data Mining},
    pages = {995-1000},
    title = {Multi-label Classification using Ensembles of Pruned Sets},
    year = {2008}
 }
 

Version:

2012.02.27

Author:

Emmanouela Stachtiari, Grigorios Tsoumakas

See Also:

Serialized Form

Field Summary

protected PrunedSets[]	ensemble The models in the ensemble
protected int	numOfModels Parameter for the number of models that constitute the ensemble
protected double	percentage Percentage of data
protected Random	rand Random number generator
protected double	threshold Parameter for the threshold of discretization of prediction output

Fields inherited from class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

baseClassifier

Fields inherited from class mulan.classifier.MultiLabelLearnerBase

featureIndices, labelIndices, numLabels

Constructor Summary

EnsembleOfPrunedSets()
Creates a new instance with default values

EnsembleOfPrunedSets(double aPercentage, int aNumOfModels, double aThreshold, int aP, PrunedSets.Strategy aStrategy, int aB, Classifier baselearner)

Method Summary

protected void	buildInternal(MultiLabelInstances trainingSet) Learner specific implementation of building the model from MultiLabelInstances training data set.
TechnicalInformation	getTechnicalInformation() 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.
String	globalInfo() Returns a string describing classifier
protected MultiLabelOutput	makePredictionInternal(Instance instance) Learner specific implementation for predicting on specified data based on trained model.

Methods inherited from class mulan.classifier.transformation.TransformationBasedMultiLabelLearner

getBaseClassifier

Methods inherited from class mulan.classifier.MultiLabelLearnerBase

build, debug, getDebug, isModelInitialized, isUpdatable, makeCopy, makePrediction, setDebug

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

threshold

protected double threshold

Parameter for the threshold of discretization of prediction output

numOfModels

protected int numOfModels

Parameter for the number of models that constitute the ensemble

percentage

protected double percentage

Percentage of data

ensemble

protected PrunedSets[] ensemble

The models in the ensemble

rand

protected Random rand

Random number generator

Constructor Detail

EnsembleOfPrunedSets

public EnsembleOfPrunedSets()

Creates a new instance with default values

EnsembleOfPrunedSets

public EnsembleOfPrunedSets(double aPercentage,
                            int aNumOfModels,
                            double aThreshold,
                            int aP,
                            PrunedSets.Strategy aStrategy,
                            int aB,
                            Classifier baselearner)

Parameters:

aNumOfModels - the number of models in the ensemble

aStrategy - pruned sets strategy

aPercentage - percentage of data to sample

aP - pruned sets parameter p

aB - pruned sets parameter b

baselearner - the base learner

aThreshold - the threshold for producing bipartitions

Method Detail

buildInternal

protected void buildInternal(MultiLabelInstances trainingSet)
                      throws Exception

Description copied from class: MultiLabelLearnerBase

Learner specific implementation of building the model from MultiLabelInstances training data set. This method is called from MultiLabelLearnerBase.build(MultiLabelInstances) method, where behavior common across all learners is applied.

Specified by:

buildInternal in class MultiLabelLearnerBase

Parameters:

trainingSet - the training data set

Throws:

Exception - if learner model was not created successfully

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Description copied from class: 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.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Overrides:

getTechnicalInformation in class TransformationBasedMultiLabelLearner

Returns:

the technical information about this class

globalInfo

public String globalInfo()

Returns a string describing classifier

Overrides:

globalInfo in class TransformationBasedMultiLabelLearner

Returns:

a description suitable for displaying

makePredictionInternal

protected MultiLabelOutput makePredictionInternal(Instance instance)
                                           throws Exception,
                                                  InvalidDataException

Description copied from class: MultiLabelLearnerBase

Learner specific implementation for predicting on specified data based on trained model. This method is called from MultiLabelLearnerBase.makePrediction(weka.core.Instance) which guards for model initialization and apply common handling/behavior.

Specified by:

makePredictionInternal in class MultiLabelLearnerBase

Parameters:

instance - the data instance to predict on

Returns:

the output of the learner for the given instance

Throws:

Exception - if an error occurs while making the prediction.

InvalidDataException - if specified instance data is invalid and can not be processed by the learner