Use weka classifiers functions LibSVM and SMO - Java Machine Learning AI

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Description

Use weka classifiers functions LibSVM and SMO

Demo Code

import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.LibSVM;
import weka.classifiers.functions.SMO;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

public class Classifier {
    public static void main(String[] args) throws Exception {
        DataSource source = new DataSource("iris.arff");
        Instances dataset = source.getDataSet();
        dataset.setClassIndex(dataset.numAttributes() - 1);
        NaiveBayes nb = new NaiveBayes();
        nb.buildClassifier(dataset);//from w w  w.  j  a  v a2 s .  c  o  m
        Evaluation eval = new Evaluation(dataset);
        eval.evaluateModel(nb, dataset);
        System.out.println(eval.toSummaryString());

        SMO svm = new SMO();
        svm.buildClassifier(dataset);
        Evaluation eval2 = new Evaluation(dataset);
        eval2.evaluateModel(svm, dataset);
        System.out.println(eval2.toSummaryString());

        LibSVM libsvm = new LibSVM();
        String[] options = new String[8];
        options[0] = "-S";
        options[1] = "0";
        options[2] = "-K";
        options[3] = "2";
        options[4] = "-G";
        options[5] = "1.0";
        options[6] = "-C";
        options[7] = "1.0";
        libsvm.setOptions(options);
        libsvm.buildClassifier(dataset);
        Evaluation eval3 = new Evaluation(dataset);
        eval3.evaluateModel(libsvm, dataset);
        System.out.println(eval3.toSummaryString());
    }
}

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