Package unifeat.featureSelection.embedded.TreeBasedMethods

Class RandomForestMethod

java.lang.Object

unifeat.featureSelection.FeatureSelection

unifeat.featureSelection.embedded.EmbeddedApproach

unifeat.featureSelection.embedded.TreeBasedMethods.TreeBasedMethods

unifeat.featureSelection.embedded.TreeBasedMethods.RandomForestMethod

public class RandomForestMethod
extends TreeBasedMethods

This java class is used to implement the random forest based method for feature selection.

Author:

Sina Tabakhi

See Also:

• TreeBasedMethods
• EmbeddedApproach
• FeatureSelection

Field Summary

Fields inherited from class unifeat.featureSelection.embedded.TreeBasedMethods.TreeBasedMethods

TEMP_PATH, TREE_TYPE

Fields inherited from class unifeat.featureSelection.embedded.EmbeddedApproach

classLabel, nameFeatures, PROJECT_PATH

Fields inherited from class unifeat.featureSelection.FeatureSelection

numClass, numFeatures, numSelectedFeature, selectedFeatureSubset, trainSet

Constructor Summary

Constructors

Constructor	Description
RandomForestMethod(Object... arguments)	Initializes the parameters
RandomForestMethod(String path, int randomForestNumFeatures, int randomForestMaxDepth, int randomForestNumIterations)	Initializes the parameters

Method Summary

Modifier and Type	Method	Description
protected String	buildClassifier(weka.core.Instances dataTrain)	Generates a classifier using input data
void	evaluateFeatures()	Starts the feature selection process by Random Forest based method
protected void	selectedFeatureSubset(String tree)	Finds the feature subset from the nodes of the created tree (Used for Random Forest)

Methods inherited from class unifeat.featureSelection.embedded.EmbeddedApproach

loadDataSet, loadDataSet, newMethod, originalFeatureSet

Methods inherited from class unifeat.featureSelection.FeatureSelection

getSelectedFeatureSubset, setNumSelectedFeature, validate

Methods inherited from class java.lang.Object

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

Constructor Details

RandomForestMethod

public RandomForestMethod(Object... arguments)

Initializes the parameters

Parameters:

arguments - array of parameter contains (path, tree type, NumFeatures, MaxDepth, NumIterations) in which path is the path of the project, tree type is the type of tree, NumFeatures is the number of randomly selected features, MaxDepth is the maximum depth of the tree, NumIterations is the number of iterations to be performed

RandomForestMethod

public RandomForestMethod(String path,
 int randomForestNumFeatures,
 int randomForestMaxDepth,
 int randomForestNumIterations)

Initializes the parameters

Parameters:

path - the path of the project

randomForestNumFeatures - the number of randomly selected features

randomForestMaxDepth - the maximum depth of the tree

randomForestNumIterations - the number of iterations to be performed

Method Details

selectedFeatureSubset

protected void selectedFeatureSubset(String tree)

Finds the feature subset from the nodes of the created tree (Used for Random Forest)

Specified by:

selectedFeatureSubset in class TreeBasedMethods

Parameters:

tree - the generated tree based on the train set

buildClassifier

protected String buildClassifier(weka.core.Instances dataTrain)

Generates a classifier using input data

Specified by:

buildClassifier in class TreeBasedMethods

Parameters:

dataTrain - the data to train the classifier

Returns:

the output of the generated classifier

evaluateFeatures

public void evaluateFeatures()

Starts the feature selection process by Random Forest based method

Specified by:

evaluateFeatures in class FeatureSelection