Package unifeat.featureSelection.filter.unsupervised

Class RSM

java.lang.Object

unifeat.featureSelection.FeatureSelection

unifeat.featureSelection.filter.FilterApproach

unifeat.featureSelection.filter.unsupervised.RSM

public class RSM
extends FilterApproach

This java class is used to implement the random subspace method(RSM) 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
RSM(int sizeSelectedFeatureSubset, int numIter, int size, int threshold, MultivariateMethodType nameApproach)	Initializes the parameters
RSM(Object... arguments)	Initializes the parameters

Method Summary

Modifier and Type	Method	Description
void	evaluateFeatures()	Starts the feature selection process by random subspace method(RSM)
String	validate()	This method returns the potential errors in the input parameters.

Methods inherited from class unifeat.featureSelection.filter.FilterApproach

newMethod

Methods inherited from class unifeat.featureSelection.FeatureSelection

getSelectedFeatureSubset, loadDataSet, loadDataSet, setNumSelectedFeature

Methods inherited from class java.lang.Object

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

Constructor Details

RSM

public RSM(Object... arguments)

Initializes the parameters

Parameters:

arguments - array of parameters contains (sizeSelectedFeatureSubset, numIter, size, threshold, nameApproach) in which sizeSelectedFeatureSubset is the number of selected features, numIter is the number of iteration in the RSM method, size is the size of the subspace, threshold is the number of selected features in each subspace, and nameApproach is the name of the multivariate approach used in the RSM

RSM

public RSM(int sizeSelectedFeatureSubset,
 int numIter,
 int size,
 int threshold,
 MultivariateMethodType nameApproach)

Initializes the parameters

Parameters:

sizeSelectedFeatureSubset - the number of selected features

numIter - the number of iteration in the RSM method

size - the size of the subspace

threshold - the number of selected features in each subspace

nameApproach - the name of the multivariate approach used in the RSM

Method Details

evaluateFeatures

public void evaluateFeatures()

Starts the feature selection process by random subspace method(RSM)

Specified by:

evaluateFeatures in class FeatureSelection

validate

public String validate()

This method returns the potential errors in the input parameters.

Overrides:

validate in class FeatureSelection

Returns:

a string contains the information about incorrect parameters