mulan.classifier.meta.thresholding
Class RCut

java.lang.Object
  mulan.classifier.MultiLabelLearnerBase
      mulan.classifier.meta.MultiLabelMetaLearner
          mulan.classifier.meta.thresholding.RCut

All Implemented Interfaces:

Serializable, MultiLabelLearner, TechnicalInformationHandler

public class RCut
extends MultiLabelMetaLearner

Classs that implements RCut(Rank-based cut). It selects the k top ranked labels for each instance, where k is a parameter provided by the user or automatically tuned.Yiming Yang: A study of thresholding strategies for text categorization. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 137 - 145, 2001.

BibTeX:

 @inproceedings{Yang2001,
    author = {Yiming Yang},
    booktitle = {Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval},
    pages = {137 - 145},
    title = {A study of thresholding strategies for text categorization},
    year = {2001},
    location = {New Orleans, Louisiana, United States}
 }
 

Version:

2010.12.14

Author:

Marios Ioannou, George Sakkas, Grigorios Tsoumakas

See Also:

Serialized Form

Field Summary

Fields inherited from class mulan.classifier.meta.MultiLabelMetaLearner

baseLearner

Fields inherited from class mulan.classifier.MultiLabelLearnerBase

featureIndices, labelIndices, numLabels

Constructor Summary

RCut()
Default constructor

RCut(MultiLabelLearner baseLearner)
Creates a new instance of RCut

RCut(MultiLabelLearner baseLearner, BipartitionMeasureBase aMeasure)
Creates a new instance of RCut

RCut(MultiLabelLearner baseLearner, BipartitionMeasureBase aMeasure, int someFolds)
Creates a new instance of RCut

Method Summary

protected void	buildInternal(MultiLabelInstances trainingData) 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 the multi-label learner.
protected MultiLabelOutput	makePredictionInternal(Instance instance) Learner specific implementation for predicting on specified data based on trained model.
void	setDebug(boolean debug) Set debugging mode.

Methods inherited from class mulan.classifier.meta.MultiLabelMetaLearner

getBaseLearner

Methods inherited from class mulan.classifier.MultiLabelLearnerBase

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

Methods inherited from class java.lang.Object

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

Constructor Detail

RCut

public RCut()

Default constructor

RCut

public RCut(MultiLabelLearner baseLearner)

Creates a new instance of RCut

Parameters:

baseLearner - the underlying multi-label learner

RCut

public RCut(MultiLabelLearner baseLearner,
            BipartitionMeasureBase aMeasure,
            int someFolds)

Creates a new instance of RCut

Parameters:

baseLearner - the underlying multi-label learner

aMeasure - measure to optimize

someFolds - cross-validation folds

RCut

public RCut(MultiLabelLearner baseLearner,
            BipartitionMeasureBase aMeasure)

Creates a new instance of RCut

Parameters:

baseLearner - the underlying multi-label learner

aMeasure - measure to optimize

Method Detail

buildInternal

protected void buildInternal(MultiLabelInstances trainingData)
                      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:

trainingData - the training data set

Throws:

Exception - if learner model was not created successfully

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Description copied from class: 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.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Specified by:

getTechnicalInformation in class MultiLabelLearnerBase

Returns:

the technical information about this class

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

setDebug

public void setDebug(boolean debug)

Description copied from class: MultiLabelLearnerBase

Set debugging mode.

Specified by:

setDebug in interface MultiLabelLearner

Overrides:

setDebug in class MultiLabelLearnerBase

Parameters:

debug - true if debug output should be printed

globalInfo

public String globalInfo()

Description copied from class: MultiLabelLearnerBase

Returns a string describing the multi-label learner.

Specified by:

globalInfo in class MultiLabelLearnerBase

Returns:

a description suitable for displaying in a future gui