Java tutorial
import it.unisa.gitdm.bean.MyClassifier; import weka.classifiers.Classifier; import weka.classifiers.bayes.NaiveBayes; import weka.classifiers.functions.Logistic; import weka.classifiers.functions.MultilayerPerceptron; import weka.classifiers.rules.DecisionTable; import weka.classifiers.trees.RandomForest; /* * To change this license header, choose License Headers in Project Properties. * To change this template file, choose Tools | Templates * and open the template in the editor. */ /** * * @author fabiano */ public class ClassifierBuilder { public static MyClassifier buildClassifier(String name) { MyClassifier toReturn = new MyClassifier(name); switch (name) { case "Decision Table Majority": toReturn.setClassifier(new DecisionTable()); break; case "Logistic Regression": toReturn.setClassifier(new Logistic()); break; case "Multi Layer Perceptron": toReturn.setClassifier(new MultilayerPerceptron()); break; case "Naive Baesian": toReturn.setClassifier(new NaiveBayes()); break; case "Random Forest": toReturn.setClassifier(new RandomForest()); break; default: break; } return toReturn; } }