Java examples for Machine Learning AI:weka
weka MetaCost Adaboost J48
import java.io.FileWriter; import java.util.ArrayList; import weka.classifiers.CostMatrix; import weka.classifiers.Evaluation; import weka.classifiers.evaluation.Prediction; import weka.classifiers.meta.AdaBoostM1; import weka.classifiers.meta.MetaCost; import weka.classifiers.trees.J48; import weka.core.Instances; import weka.core.converters.ConverterUtils.DataSource; import au.com.bytecode.opencsv.CSVWriter; public class Main { public static void main(String[] args) throws Exception { Instances train = DataSource read("./train1.arff"); int cid1 = train.numAttributes() - 1; train.setClassIndex(cid1);/*w w w .j ava2 s . c o m*/ Instances validation = DataSource read("./validation1.arff"); int cid2 = validation.numAttributes() - 1; validation.setClassIndex(cid2); Instances test = DataSource read("./test1.arff"); int cid3 = test.numAttributes() - 1; test.setClassIndex(cid3); J48 jtree = new J48(); AdaBoostM1 btree = new AdaBoostM1(); btree.setClassifier(jtree); //btree.buildClassifier(train); //MetaCost CostMatrix cm = new CostMatrix(2); cm.setElement(0, 1, 1); cm.setElement(1, 0, 18); cm.setElement(0, 0, 0); cm.setElement(1, 1, 0); MetaCost tree = new MetaCost(); tree.setClassifier(btree); tree.setCostMatrix(cm); tree.buildClassifier(train); Evaluation eval = new Evaluation(train); eval.evaluateModel(tree, validation); System.out.println(eval.toSummaryString("\nResults_RF\n\n", false)); System.out.println(eval.toClassDetailsString()); System.out.println(eval.toMatrixString()); ArrayList<Prediction> al = eval.predictions(); ArrayList<String[]> as = new ArrayList<String[]>(al.size()); for (int i = 0; i < al.size(); i++) { String[] s = new String[1]; s[0] = al.get(i).toString(); s[0] = s[0].substring(9, 11); as.add(s); } ArrayList<String[]> li = new ArrayList<String[]>(al.size()); li.addAll(as); String csv = "./output.csv"; CSVWriter writer = new CSVWriter(new FileWriter(csv)); writer.writeAll(li); writer.close(); } }