it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification

Class MultiLabelClassificationLearning

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.classification.multiclassification.MultiLabelClassificationLearning

All Implemented Interfaces:

ClassificationLearningAlgorithm, LearningAlgorithm, MetaLearningAlgorithm

public class MultiLabelClassificationLearning
extends Object
implements ClassificationLearningAlgorithm, MetaLearningAlgorithm

It is a meta algorithm that operates applying a multi label learning strategy over the base learning algorithm which is intended to be a binary learner. A multi label classification task is a multiclass problem in which each instance can belongs to zero, one or multiple classes. The multi label strategy will learn N different classifiers, where N is the number of classes involved in the dataset. In this strategy each classifier is learned by considering in turn the examples of a single class as positives, while all the other examples are considered as negative.

NOTE: the base learning algorithm must provide a duplicate method which properly works

Author:

Simone Filice

Constructor Summary

Constructors

Constructor and Description
MultiLabelClassificationLearning()

Method Summary

Methods

Modifier and Type	Method and Description
MultiLabelClassificationLearning	duplicate() This method will duplicate the current Learning algorithm
LearningAlgorithm	getBaseAlgorithm() This method will return the base algorithm.
List<Label>	getLabels() Returns the labels to be learned
MultiLabelClassifier	getPredictionFunction() This method returns the learned PredictionFunction.
void	learn(Dataset dataset) This method will cause the meta-learning algorithm to learn N classifiers, where N is the number of classes in the dataset.
void	reset() This method will cause the reset of all the base algorithms
void	setBaseAlgorithm(LearningAlgorithm baseAlgorithm) This method will set the type of the base algorithms to be learned.
void	setLabels(List<Label> labels) Set the labels associated to this multi-classifier.

Methods inherited from class java.lang.Object

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

Constructor Detail

MultiLabelClassificationLearning

public MultiLabelClassificationLearning()

Method Detail

setLabels

public void setLabels(List<Label> labels)

Set the labels associated to this multi-classifier.

Specified by:

setLabels in interface LearningAlgorithm

Parameters:

labels - the labels representing the concept to be learned

getLabels

public List<Label> getLabels()

Returns the labels to be learned

Specified by:

getLabels in interface LearningAlgorithm

Returns:

the labels to be learned

learn

public void learn(Dataset dataset)

This method will cause the meta-learning algorithm to learn N classifiers, where N is the number of classes in the dataset.

Specified by:

learn in interface LearningAlgorithm

Parameters:

dataset - the training data

reset

public void reset()

This method will cause the reset of all the base algorithms

Specified by:

reset in interface LearningAlgorithm

getPredictionFunction

public MultiLabelClassifier getPredictionFunction()

This method returns the learned PredictionFunction. It is of type MultiLabelClassifier.

Specified by:

getPredictionFunction in interface ClassificationLearningAlgorithm

Specified by:

getPredictionFunction in interface LearningAlgorithm

Returns:

the predictionFunction learned during the training process

setBaseAlgorithm

public void setBaseAlgorithm(LearningAlgorithm baseAlgorithm)

This method will set the type of the base algorithms to be learned.

Specified by:

setBaseAlgorithm in interface MetaLearningAlgorithm

Parameters:

baseAlgorithm - the baseAlgorithm to set

getBaseAlgorithm

public LearningAlgorithm getBaseAlgorithm()

This method will return the base algorithm.

Specified by:

getBaseAlgorithm in interface MetaLearningAlgorithm

Returns:

the base algorithm

duplicate

public MultiLabelClassificationLearning duplicate()

This method will duplicate the current Learning algorithm

Specified by:

duplicate in interface LearningAlgorithm