mulan.classifier.meta
Class RAkEL

java.lang.Object
  mulan.classifier.MultiLabelLearnerBase
      mulan.classifier.meta.MultiLabelMetaLearner
          mulan.classifier.meta.RAkEL

All Implemented Interfaces:

Serializable, MultiLabelLearner, TechnicalInformationHandler

public class RAkEL
extends MultiLabelMetaLearner

Class implementing a generalized version of the RAkEL (RAndom k-labELsets) algorithm. For more information, see

Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas (2011). Random k-Labelsets for Multi-Label Classification. IEEE Transactions on Knowledge and Data Engineering. 23(7):1079-1089.

BibTeX:

 @article{Tsoumakas2011,
    author = {Grigorios Tsoumakas and Ioannis Katakis and Ioannis Vlahavas},
    journal = {IEEE Transactions on Knowledge and Data Engineering},
    number = {7},
    pages = {1079-1089},
    title = {Random k-Labelsets for Multi-Label Classification},
    volume = {23},
    year = {2011}
 }
 

Version:

2012.1.27

Author:

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

RAkEL()
Default constructor

RAkEL(MultiLabelLearner baseLearner)
Creates an instance based on a given multi-label learner

RAkEL(MultiLabelLearner baseLearner, int models, int subset)
Creates an instance given a specific multi-label learner, number of models and size of subsets

RAkEL(MultiLabelLearner baseLearner, int models, int subset, double threshold)
Creates an instance given a specific multi-label learner, number of models, size of subsets and threshold

Method Summary

static int	binomial(int n, int m) The binomial function
protected void	buildInternal(MultiLabelInstances trainingData) Learner specific implementation of building the model from MultiLabelInstances training data set.
int	getNumModels() Returns the number of models
int	getSizeOfSubset() Returns the size of the subsets
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.
void	setNumModels(int models) Sets the number of models
void	setSeed(int x) Sets the seed for random number generation
void	setSizeOfSubset(int size) Sets the size of the subsets

Methods inherited from class mulan.classifier.meta.MultiLabelMetaLearner

getBaseLearner

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

Constructor Detail

RAkEL

public RAkEL()

Default constructor

RAkEL

public RAkEL(MultiLabelLearner baseLearner)

Creates an instance based on a given multi-label learner

Parameters:

baseLearner - the multi-label learner

RAkEL

public RAkEL(MultiLabelLearner baseLearner,
             int models,
             int subset)

Creates an instance given a specific multi-label learner, number of models and size of subsets

Parameters:

baseLearner - a multi-label learner

models - a number of models

subset - a size of subsets

RAkEL

public RAkEL(MultiLabelLearner baseLearner,
             int models,
             int subset,
             double threshold)

Creates an instance given a specific multi-label learner, number of models, size of subsets and threshold

Parameters:

baseLearner - a multi-label learner

models - a number of models

subset - a size of subsets

threshold - a threshold

Method Detail

getTechnicalInformation

public 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.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Specified by:

getTechnicalInformation in class MultiLabelLearnerBase

Returns:

the technical information about this class

setSeed

public void setSeed(int x)

Sets the seed for random number generation

Parameters:

x - the seed

setSizeOfSubset

public void setSizeOfSubset(int size)

Sets the size of the subsets

Parameters:

size - the size of the subsets

getSizeOfSubset

public int getSizeOfSubset()

Returns the size of the subsets

Returns:

the size of the subsets

setNumModels

public void setNumModels(int models)

Sets the number of models

Parameters:

models - number of models

getNumModels

public int getNumModels()

Returns the number of models

Returns:

number of models

binomial

public static int binomial(int n,
                           int m)

The binomial function

Parameters:

n -

m -

Returns:

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

makePredictionInternal

protected MultiLabelOutput makePredictionInternal(Instance instance)
                                           throws Exception

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

globalInfo

public String globalInfo()

Returns a string describing classifier

Specified by:

globalInfo in class MultiLabelLearnerBase

Returns:

a description suitable for displaying