Use weka classifiers Dagging - Java Machine Learning AI

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Description

Use weka classifiers Dagging

Demo Code

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.Logistic;
import weka.classifiers.functions.SMO;
import weka.classifiers.functions.SimpleLinearRegression;
import weka.classifiers.functions.VotedPerceptron;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.classifiers.meta.CVParameterSelection;
import weka.classifiers.meta.Dagging;
import weka.classifiers.meta.GridSearch;
import weka.classifiers.mi.CitationKNN;
import weka.classifiers.misc.SerializedClassifier;
import weka.classifiers.rules.JRip;
import weka.classifiers.trees.ADTree;
import weka.classifiers.trees.BFTree;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.NBTree;
import weka.core.Instances;
import weka.core.Utils;

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

public class UntunedDagging {

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

        // load data sets
        Instances train = new Instances(new BufferedReader(new FileReader(
                "hypothyroid2_train.arff")));
        Instances test = new Instances(new BufferedReader(new FileReader(
                "hypothyroid2_test.arff")));
        train.setClassIndex(train.numAttributes() - 1);
        test.setClassIndex(test.numAttributes() - 1);
        CVParameterSelection ps = new CVParameterSelection();
        ps.setClassifier(new Dagging());


        Dagging cls = new Dagging();


        cls.buildClassifier(train);/*from w w  w . j  a va 2  s  .c  om*/
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(cls, test);
        Double error_c = eval.errorRate();
        System.out.println(error_c);

    }
}

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