List of usage examples for weka.classifiers.rules M5Rules buildClassifier
@Override public void buildClassifier(Instances data) throws Exception
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 www. ja v a 2 s . co 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()); }