Example usage for weka.attributeSelection ClassifierSubsetEval ClassifierSubsetEval

Introduction

In this page you can find the example usage for weka.attributeSelection ClassifierSubsetEval ClassifierSubsetEval.

Prototype

ClassifierSubsetEval

Source Link

Usage

From source file:ca.uottawa.balie.WekaAttributeSelection.java

License:Open Source License

/**
 * Select the top attributes// w  w  w .  jav a  2 s  .  c o  m
 */
public void Select(boolean pi_Debug) {
    Instances insts = m_DummyLearner.GetTrainInstances();

    try {
        ASEvaluation eval = null;
        ASSearch search = null;

        if (m_Evaluator == WEKA_CHI_SQUARE) {
            eval = new ChiSquaredAttributeEval();
            search = new Ranker();
            ((Ranker) search).setNumToSelect(m_NumAttributes);
        } else if (m_Evaluator == WEKA_INFO_GAIN) {
            eval = new InfoGainAttributeEval();
            search = new Ranker();
            ((Ranker) search).setNumToSelect(m_NumAttributes);
        } else if (m_Evaluator == WEKA_WRAPPER) {
            eval = new ClassifierSubsetEval();
            ((ClassifierSubsetEval) eval).setClassifier(new NaiveBayes());
            search = new Ranker(); // TODO: use something else than ranker
            ((Ranker) search).setNumToSelect(m_NumAttributes);
        } else if (m_Evaluator == WEKA_SYM_UNCERT) {
            eval = new SymmetricalUncertAttributeEval();
            search = new Ranker();
            ((Ranker) search).setNumToSelect(m_NumAttributes);
        } else if (m_Evaluator == WEKA_SVM) {
            eval = new SVMAttributeEval();
            search = new Ranker();
            ((Ranker) search).setNumToSelect(m_NumAttributes);
        } else if (m_Evaluator == WEKA_RELIEF) {
            eval = new ReliefFAttributeEval();
            search = new Ranker();
            ((Ranker) search).setNumToSelect(m_NumAttributes);
        } else if (m_Evaluator == WEKA_ONER) {
            eval = new OneRAttributeEval();
            search = new Ranker();
            ((Ranker) search).setNumToSelect(m_NumAttributes);
        }

        m_AttributeSelection = new AttributeSelection();
        m_AttributeSelection.setEvaluator(eval);
        m_AttributeSelection.setSearch(search);

        m_AttributeSelection.SelectAttributes(insts);
        if (pi_Debug)
            System.out.println(m_AttributeSelection.toResultsString());

    } catch (Exception e) {
        System.err.println(e.getMessage());
    }

}

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