java - 如何在Weka中实现AdaBoostM1？

我正在使用Weka 3.6.13（发布时为最新），但似乎无法让AdaBoostM1选择分类器。我想包括AdaBoostM1以及基本分类器的选项。但是函数setClassifier（）似乎并不存在于JAR中，而是存在于Docs中。

import java.io.*;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.evaluation.NominalPrediction;
import weka.classifiers.meta.AdaBoostM1;
import weka.classifiers.trees.RandomForest;
import weka.core.FastVector;
import weka.core.Instances;

public class ULCWekaTest
{

   public static Evaluation classify(Classifier model,
                                     Instances trainingSet, Instances testingSet)
      throws Exception
   {
      Evaluation evaluation = new Evaluation(trainingSet);

      model.buildClassifier(trainingSet);
      evaluation.evaluateModel(model, testingSet);

      return evaluation;
   }

   public static double calculateAccuracy(FastVector predictions)
   {
      double correct = 0;

      for(int i = 0; i < predictions.size(); i++)
      {
         NominalPrediction np = (NominalPrediction) predictions.elementAt(i);
         if(np.predicted() == np.actual())
            correct++;
      }

      return 100 * correct / predictions.size();
   }

   public static void main(String[] args) throws Exception
   {
      String basepath =
         "/Users/Ebe/Pantheon/Ares/PhD/Course Work/MLT/MLT-Lab/Mini Project/Implementation/";

      Instances trainData;
      try(BufferedReader reader =
         new BufferedReader(
            new FileReader(basepath + "train80.arff")))
      {
         trainData = new Instances(reader);
         reader.close();
      }
      trainData.setClassIndex(0);

      Instances testData;
      try(BufferedReader reader =
         new BufferedReader(
            new FileReader(basepath + "test80.arff")))
      {
         testData = new Instances(reader);
         reader.close();
      }
      testData.setClassIndex(0);

      Classifier forest, adaboost;
      String[] options = new String[2];

      for(int i = 100; i < 500; i+=2)
      {
         for(int s = 6; s <= 6; s++)
         {
            adaboost = new AdaBoostM1();
            forest = new RandomForest();
            //for(String str : forest.getOptions())
               // System.out.println(str);
            options[0] = "-I";
            options[1] = ""+i;
            forest.setOptions(options);

            options[0] = "-S";
            options[1] = "" + s;
            forest.setOptions(options);

            adaboost.setClassifier(forest); // <-- COMPILER ERROR AT THIS POINT
            // Collect every group of predictions for current model in a FastVector
            FastVector predictions = new FastVector();
            // For each training-testing split pair, train and test the classifier
            Evaluation validation =
               classify(adaboost, trainData, testData);
            predictions.appendElements(validation.predictions());

            // Calculate overall accuracy of current classifier on all splits
            double accuracy = calculateAccuracy(predictions);
         // Print current classifier's name and accuracy in a complicated,
            // but nice-looking way.
            System.out.println("-I " + i + " -S " + s + ": " + String.format(
               "%.2f%%", accuracy));
         }
      }
   }
}

难道我做错了什么？如何解决这个问题？谁能解释或至少提供一个Java示例AdaBoostM1-Weka代码的链接？

最佳答案

正在使用Classifier类。这是AdaBoostM1和RandomForest分类器的基类。因此，您将无法访问selectClassifier()方法，因为它是从Classifier派生的类访问的。

改变这条线

Classifier forest, adaboost;

至

Classifier forest;
AdaBoostM1 adaboost;

解决了问题。