use weka classifiers JRip - Java Machine Learning AI

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Description

use weka classifiers JRip

Demo Code

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.SMO;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.classifiers.meta.CVParameterSelection;
import weka.classifiers.meta.Dagging;
import weka.classifiers.meta.GridSearch;
import weka.classifiers.misc.SerializedClassifier;
import weka.classifiers.rules.JRip;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.Utils;

import java.io.BufferedReader;
import java.io.FileReader;

public class TuningJRip {

    public static void main(String[] args) throws Exception {

        Instances train = new Instances(new BufferedReader(new FileReader(
                "hypothyroid_train.arff")));
        Instances test = new Instances(new BufferedReader(new FileReader(
                "hypothyroid_test.arff")));
        train.setClassIndex(train.numAttributes() - 1);
        test.setClassIndex(test.numAttributes() - 1);
        CVParameterSelection ps = new CVParameterSelection();
        ps.setClassifier(new J48());
        ps.addCVParameter("F 1 5 5");
        ps.addCVParameter("N 1 5 5");
        ps.addCVParameter("O 1 5 5");
        ps.addCVParameter("S 1 5 5");

        JRip cls = new JRip();
        cls.setFolds(5);/*from w  ww  .  j av  a  2  s.  com*/
        cls.setMinNo(1);
        cls.setOptimizations(5);
        cls.setSeed(1);

        cls.buildClassifier(train);
        ps.buildClassifier(train);

        System.out.println(Utils.joinOptions(ps.getBestClassifierOptions()));
        weka.core.SerializationHelper.write(
                "hypothyroid.model", cls);
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(cls, test);
        Double error_c = eval.errorRate();
        System.out.println(error_c);

    }
}

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