Weka开发［6］－LibSVM
? ? ? ?首先要提的是LibSVM是一个库，Lib很明显是library的缩写，有些人不知道怎么会认为它是一种算法。它是由中国台湾的，记住是中国的台湾(⊙o⊙)…，Chih-Chung Chang和Chih-Jen Lin等人开发的，他们用多种语言实现写了LibSVM。
? ? ? ?我把这一篇放到Weka开发里讲，主要讲它怎么和Weka结合，Weka中并不是没有SVM算法，Weka中有SMO算法的实现。
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Weka?and?LibSVM?are two efficient software tools for building SVM classifiers. Each one of these two tools has its points of strength and weakness. Weka has a GUI and produces many useful statistics (e.g. confusion matrix, precision, recall, F-measure, and ROC scores). LibSVM runs much faster than Weka SMO and supports several SVM methods (e.g. One-class SVM, nu-SVM, and R-SVM). Weka LibSVM (WLSVM) combines the merits of the two tools. WLSVM can be viewed as an implementation of the LibSVM running under Weka environment.
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这一段是我从拷贝来的，请注意里面有一句话，LibSVM运行的比Weka里的SMO快的多，如果你敢用SMO算法去训练大数据集，你就明白天荒地老的真实含意了。其它和Weka结合的最主要的原因，我认为是，我们开始的时候往往都是用别的算法去试着做实验的（或者本来就需要多种基分类器），比如Na?ve Bayes(LibSVM是比SMO快的多，可是比起来Na?ve Bayes，它还是蜗牛)，到最后又想试试LibSVM，这时就需要LibSVM库。
还有一点也是容易误解的就是Weka里的高版本，里面是有LibSVM这个分类器，但你如果直接想运行是会出错的，提示你没有设置路径。原因是：WLSVM can be viewed as an implementation of the LibSVM running under Weka environment.
先把LibSVM下载下来，上次竟然有人问我在哪下载，这种问题，我真是不想回答，不就是google一下吗？网址也放上：http://www.csie.ntu.edu.tw/~cjlin/libsvm/，下载WLSVM，解压后Lib文件夹下有一个LibSVM.jar的包，用和导入Weka.jar包相同的方式导入就好了，然后使用LibSVM和使用以前任何一种分类器的方式都是一样的。这里顺便带大家复习以下特征选择。
package com.cizito.weka.study;import java.util.Random;import weka.attributeSelection.CfsSubsetEval;import weka.attributeSelection.GreedyStepwise;import weka.classifiers.Evaluation;import weka.classifiers.bayes.NaiveBayes;import weka.classifiers.functions.LibLINEAR;import weka.classifiers.functions.LibSVM;import weka.classifiers.meta.AttributeSelectedClassifier;import weka.classifiers.trees.J48;import weka.core.Instances;import weka.core.converters.ConverterUtils.DataSource;import weka.filters.Filter;import weka.filters.supervised.attribute.AttributeSelection;/** * @author zhangwei * */public class LibSVMTest {private Instances m_instances = null;private Instances selectedIns;public static void main( String[] args ) throws Exception {LibSVMTest filter = new LibSVMTest(); filter.getFileInstances( "D:/ProgramFiles/Weka-3-6/data/soybean.arff"); filter.selectAttUseFilter(); filter.selectAttUseMC();} public void getFileInstances( String fileName ) throws Exception { DataSource frData = new DataSource( fileName ); m_instances = frData.getDataSet(); m_instances.setClassIndex( m_instances.numAttributes() - 1 ); } public void selectAttUseFilter() throws Exception { AttributeSelection filter = new AttributeSelection(); // package weka.filters.supervised.attribute! CfsSubsetEval eval = new CfsSubsetEval(); GreedyStepwise search = new GreedyStepwise(); filter.setEvaluator(eval); filter.setSearch(search); filter.setInputFormat( m_instances ); System.out.println( "number of instance attribute = " + m_instances.numAttributes() ); selectedIns = Filter.useFilter( m_instances, filter); System.out.println( "number of selected instance attribute = " + selectedIns.numAttributes() ); for( int i = 0; i < selectedIns.numInstances(); i++ ) { System.out.println( selectedIns.instance( i ) ); } } public void selectAttUseMC() throws Exception { AttributeSelectedClassifier classifier = new AttributeSelectedClassifier(); CfsSubsetEval eval = new CfsSubsetEval(); GreedyStepwise search = new GreedyStepwise(); J48 base = new J48(); NaiveBayes nb = new NaiveBayes(); LibSVM svm = new LibSVM(); LibLINEAR linear = new LibLINEAR(); classifier.setClassifier( svm ); classifier.setEvaluator( eval ); classifier.setSearch( search ); // 10-fold cross-validation Evaluation evaluation = new Evaluation( selectedIns ); evaluation.crossValidateModel(svm, m_instances, 10, new Random(1)); System.out.println("正确率为：" + (1-evaluation.errorRate())); System.out.println( evaluation.toSummaryString()); }}? ?好人做到底吧，顺便把LibSVM包上传，然后告诉大家其实新版的WEKA里面已经包含了LibSVM这个分类器了