Package unifeat.classifier.evaluation.wekaClassifier

Class TrainTestEvaluation

java.lang.Object

unifeat.classifier.evaluation.wekaClassifier.TrainTestEvaluation

public class TrainTestEvaluation
extends Object

This java class is used to apply the classifiers for computing the performance of the feature selection methods. The classifiers have been implemented as the Weka software. The training-test sets method is used for evaluating classifiers.

Author:

Sina Tabakhi

Constructor Summary

Constructors

Constructor	Description
TrainTestEvaluation()

Method Summary

Modifier and Type	Method	Description
static Criteria	dTree(String pathTrainData, String pathTestData, double confidenceValue, int minNumSampleInLeaf)	This method builds and evaluates the decision tree(DT) classifier.
static Criteria	kNN(String pathTrainData, String pathTestData, int kNNValue)	This method builds and evaluates the k-nearest neighbours(knn) classifier.
static Criteria	naiveBayes(String pathTrainData, String pathTestData)	This method builds and evaluates the naiveBayes(NB) classifier.
static Criteria	SVM(String pathTrainData, String pathTestData, SVMKernelType svmKernel, double c)	This method builds and evaluates the support vector machine(SVM) classifier.

Methods inherited from class java.lang.Object

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

Constructor Details

TrainTestEvaluation

public TrainTestEvaluation()

Method Details

SVM

public static Criteria SVM(String pathTrainData,
 String pathTestData,
 SVMKernelType svmKernel,
 double c)

This method builds and evaluates the support vector machine(SVM) classifier. The SMO is used as the SVM classifier implemented in the Weka software.

Parameters:

pathTrainData - the path of the train set

pathTestData - the path of the test set

svmKernel - the kernel to use

c - the complexity parameter C

Returns:

the different criteria values

See Also:

• Criteria

naiveBayes

public static Criteria naiveBayes(String pathTrainData,
 String pathTestData)

This method builds and evaluates the naiveBayes(NB) classifier. The naiveBayes is used as the NB classifier implemented in the Weka software.

Parameters:

pathTrainData - the path of the train set

pathTestData - the path of the test set

Returns:

the different criteria values

See Also:

• Criteria

dTree

public static Criteria dTree(String pathTrainData,
 String pathTestData,
 double confidenceValue,
 int minNumSampleInLeaf)

This method builds and evaluates the decision tree(DT) classifier. The j48 is used as the DT classifier implemented in the Weka software.

Parameters:

pathTrainData - the path of the train set

pathTestData - the path of the test set

confidenceValue - The confidence factor used for pruning

minNumSampleInLeaf - The minimum number of instances per leaf

Returns:

the different criteria values

See Also:

• Criteria

kNN

public static Criteria kNN(String pathTrainData,
 String pathTestData,
 int kNNValue)

This method builds and evaluates the k-nearest neighbours(knn) classifier. The IBk is used as the knn classifier implemented in the Weka software.

Parameters:

pathTrainData - the path of the train set

pathTestData - the path of the test set

kNNValue - the number of neighbours to use

Returns:

the different criteria values

See Also:

• Criteria