在java代码中调用weka,使用特征选择

package com.endual.paper.service.impls;
import weka.attributeSelection.*;
import weka.core.*;
import weka.core.converters.ConverterUtils.*;
import weka.classifiers.*;
import weka.classifiers.meta.*;
import weka.classifiers.trees.*;
import weka.filters.*;

import java.util.*;

/**
 * performs attribute selection using CfsSubsetEval and GreedyStepwise
 * (backwards) and trains J48 with that. Needs 3.5.5 or higher to compile.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 */
public class AttributeSelectionTest {

  /**
   * uses the meta-classifier
   */
  protected static void useClassifier(Instances data) throws Exception {
    System.out.println("\n1. Meta-classfier");
    AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
    CfsSubsetEval eval = new CfsSubsetEval();
    GreedyStepwise search = new GreedyStepwise();
    search.setSearchBackwards(true);
    J48 base = new J48();
    classifier.setClassifier(base);
    classifier.setEvaluator(eval);
    classifier.setSearch(search);
    Evaluation evaluation = new Evaluation(data);
    evaluation.crossValidateModel(classifier, data, 10, new Random(1));
    System.out.println(evaluation.toSummaryString());
  }

  /**
   * uses the filter
   */
  protected static void useFilter(Instances data) throws Exception {
    System.out.println("\n2. Filter");
    weka.filters.supervised.attribute.AttributeSelection filter = new weka.filters.supervised.attribute.AttributeSelection();
    CfsSubsetEval eval = new CfsSubsetEval();
    GreedyStepwise search = new GreedyStepwise();
    search.setSearchBackwards(true);
    filter.setEvaluator(eval);
    filter.setSearch(search);
    filter.setInputFormat(data);
    Instances newData = Filter.useFilter(data, filter);
    System.out.println(newData);
  }

  /**
   * uses the low level approach
   */
  protected static void useLowLevel(Instances data) throws Exception {
    System.out.println("\n3. Low-level");
    AttributeSelection attsel = new AttributeSelection();
    CfsSubsetEval eval = new CfsSubsetEval();
    GreedyStepwise search = new GreedyStepwise();
    search.setSearchBackwards(true);
    attsel.setEvaluator(eval);
    attsel.setSearch(search);
    attsel.SelectAttributes(data);
    int[] indices = attsel.selectedAttributes();
    System.out.println("selected attribute indices (starting with 0):\n" + Utils.arrayToString(indices));
  }

  /**
   * takes a dataset as first argument
   *
   * @param args        the commandline arguments
   * @throws Exception  if something goes wrong
   */
  public static void main(String[] args) throws Exception {
	
	 args = new String[1] ; 
	 args[0] = "weka_data\\data\\src_data\\paper.arff" ; //导入文件
	  
    // load data
    System.out.println("\n0. Loading data");
    DataSource source = new DataSource(args[0]);
    Instances data = source.getDataSet();
    if (data.classIndex() == -1)
      data.setClassIndex(data.numAttributes() - 1);

    // 1. meta-classifier
    useClassifier(data);

    // 2. filter
    useFilter(data);

    // 3. low-level
    useLowLevel(data);
  }
}

 

 

   项目结构图：肯定有人会问，这个训练文件是放在哪里的，文件夹怎样建？

 

 

 

 

 

运行的效果：因为涉及到论文的数据，用粗线覆盖了，抱歉无法提供完成的运行的结果