Example usage for weka.classifiers.rules M5Rules M5Rules

Introduction

In this page you can find the example usage for weka.classifiers.rules M5Rules M5Rules.

Prototype

public M5Rules() 

Source Link

Document

Constructor

Usage

From source file:epsi.i5.datamining.Weka.java

public void excutionAlgo() throws FileNotFoundException, IOException, Exception {
    BufferedReader reader = new BufferedReader(new FileReader("src/epsi/i5/data/" + fileOne + ".arff"));
    Instances data = new Instances(reader);
    reader.close();//from   w  w  w .  j  av  a2s .c  o m
    //System.out.println(data.attribute(0));
    data.setClass(data.attribute(0));
    NaiveBayes NB = new NaiveBayes();
    NB.buildClassifier(data);
    Evaluation naiveBayes = new Evaluation(data);
    naiveBayes.crossValidateModel(NB, data, 10, new Random(1));
    naiveBayes.evaluateModel(NB, data);
    //System.out.println(test.confusionMatrix() + "1");
    //System.out.println(test.correct() + "2");
    System.out.println("*****************************");
    System.out.println("******** Naive Bayes ********");
    System.out.println(naiveBayes.toMatrixString());
    System.out.println("*****************************");
    System.out.println("**** Pourcentage Correct ****");
    System.out.println(naiveBayes.pctCorrect());
    System.out.println("");
    J48 j = new J48();
    j.buildClassifier(data);
    Evaluation jeval = new Evaluation(data);
    jeval.crossValidateModel(j, data, 10, new Random(1));
    jeval.evaluateModel(j, data);
    System.out.println("*****************************");
    System.out.println("************ J48 ************");
    System.out.println(jeval.toMatrixString());
    System.out.println("*****************************");
    System.out.println("**** Pourcentage Correct ****");
    System.out.println(jeval.pctCorrect());
    System.out.println("");
    DecisionTable DT = new DecisionTable();
    DT.buildClassifier(data);
    Evaluation decisionTable = new Evaluation(data);
    decisionTable.crossValidateModel(DT, data, 10, new Random(1));
    decisionTable.evaluateModel(DT, data);
    System.out.println("*****************************");
    System.out.println("******* DecisionTable *******");
    System.out.println(decisionTable.toMatrixString());
    System.out.println("*****************************");
    System.out.println("**** Pourcentage Correct ****");
    System.out.println(decisionTable.pctCorrect());
    System.out.println("");
    OneR OR = new OneR();
    OR.buildClassifier(data);
    Evaluation oneR = new Evaluation(data);
    oneR.crossValidateModel(OR, data, 10, new Random(1));
    oneR.evaluateModel(OR, data);
    System.out.println("*****************************");
    System.out.println("************ OneR ***********");
    System.out.println(oneR.toMatrixString());
    System.out.println("*****************************");
    System.out.println("**** Pourcentage Correct ****");
    System.out.println(oneR.pctCorrect());

    //Polarit
    data.setClass(data.attribute(1));
    System.out.println("");
    M5Rules MR = new M5Rules();
    MR.buildClassifier(data);
    Evaluation m5rules = new Evaluation(data);
    m5rules.crossValidateModel(MR, data, 10, new Random(1));
    m5rules.evaluateModel(MR, data);
    System.out.println("*****************************");
    System.out.println("********** M5Rules **********");
    System.out.println(m5rules.correlationCoefficient());

    System.out.println("");
    LinearRegression LR = new LinearRegression();
    LR.buildClassifier(data);
    Evaluation linearR = new Evaluation(data);
    linearR.crossValidateModel(LR, data, 10, new Random(1));
    linearR.evaluateModel(LR, data);
    System.out.println("*****************************");
    System.out.println("********** linearR **********");
    System.out.println(linearR.correlationCoefficient());
}

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