Package unifeat.featureSelection.filter.supervised

Class MRMR

java.lang.Object

unifeat.featureSelection.FeatureSelection

unifeat.featureSelection.filter.FilterApproach

unifeat.featureSelection.filter.supervised.MRMR

public class MRMR
extends FilterApproach

This java class is used to implement the minimal redundancy maximal relevance (mRMR) method.

Author:

Sina Tabakhi

See Also:

• FilterApproach
• FeatureSelection

Field Summary

Fields inherited from class unifeat.featureSelection.FeatureSelection

numClass, numFeatures, numSelectedFeature, selectedFeatureSubset, trainSet

Constructor Summary

Constructors

Constructor	Description
MRMR(int sizeSelectedFeatureSubset)	Initializes the parameters
MRMR(Object... arguments)	Initializes the parameters

Method Summary

Modifier and Type	Method	Description
void	evaluateFeatures()	Starts the feature selection process by minimal redundancy maximal relevance (mRMR) method

Methods inherited from class unifeat.featureSelection.filter.FilterApproach

newMethod

Methods inherited from class unifeat.featureSelection.FeatureSelection

getSelectedFeatureSubset, loadDataSet, loadDataSet, setNumSelectedFeature, validate

Methods inherited from class java.lang.Object

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

Constructor Details

MRMR

public MRMR(Object... arguments)

Initializes the parameters

Parameters:

arguments - array of parameters contains (sizeSelectedFeatureSubset) in which sizeSelectedFeatureSubset is the number of selected features

MRMR

public MRMR(int sizeSelectedFeatureSubset)

Initializes the parameters

Parameters:

sizeSelectedFeatureSubset - the number of selected features

Method Details

evaluateFeatures

public void evaluateFeatures()

Starts the feature selection process by minimal redundancy maximal relevance (mRMR) method

Specified by:

evaluateFeatures in class FeatureSelection