在Eclipse下，采用mulan多标签分类软件进行一个简单的测试实验

    万事具备，只欠东风。在前几篇的博文中一直在阐述如何准备mulan的输入文件，此处将简单介绍一个如何利用

mulan在Eclipse下进行实验。

（1）新建工程Test_Mulan,新建类test_mulan,按照前几篇博文的方法加入mulan的jar包。

import mulan.classifier.lazy.MLkNN;
import mulan.classifier.lazy.IBLR_ML;
import mulan.data.MultiLabelInstances;
import mulan.evaluation.Evaluation;
import mulan.evaluation.Evaluator;
public class test_mulan {
	/**
	 * Make a test with mulan in eclipse
	 *
	 * @author WuQiang
	 * Email:[email protected]
	 */
	
public static void main(String[] args) throws Exception {
		
		String arffFile_train = "E:/methods/Eclipse/Create_Arrf/data/traindata_ch.arff";
		String xmlFile_train ="E:/methods/Eclipse/Create_Arrf/data/traindata_ch.xml";
		String arffFile_test = "E:/methods/Eclipse/Create_Arrf/data/testdata_ch.arff";
		String xmlFile_test ="E:/methods/Eclipse/Create_Arrf/data/testdata_ch.xml";
		MultiLabelInstances data_train = null;
		MultiLabelInstances data_test = null;
	    data_train = new MultiLabelInstances(arffFile_train, xmlFile_train);
		data_test = new MultiLabelInstances(arffFile_test, xmlFile_test);
		//RAkEL learner1 = new RAkEL(new LabelPowerset(new J48()));
		Evaluator eval = new Evaluator();
		Evaluation results;
		MLkNN mlknn=new MLkNN();
		IBLR_ML iblr_ml=new IBLR_ML();
		mlknn.build(data_train);
		iblr_ml.build(data_train);
		results=eval.evaluate(mlknn,data_test);
		System.out.println(results);
		results=eval.evaluate(iblr_ml,data_test);
		System.out.println(results);
		System.out.println("finished");
		//double [] pro = null;
		//MultiLabelOutput output= new MultiLabelOutput(pro);
		//System.out.println(output.getConfidences());
		//results.toCSV();
		/*int numFolds = 10;
		results = eval.crossValidate(learner1, data_train, numFolds);
		System.out.println(results);
		results = eval.crossValidate(learner2, data_train, numFolds);
		System.out.println(results);*/	    
	}
}

 只要相应的输入文件已经准备好，那么实验就非常简单。