数据挖掘-基于贝叶斯算法及KNN算法的newsgroup18828文本分类器的JAVA实现（上）

(update 2012.12.28 关于本项目下载及运行的常见问题 FAQ见 newsgroup18828文本分类器、文本聚类器、关联分析频繁模式挖掘算法的Java实现工程下载及运行FAQ )

本文主要内容如下：
对newsgroup文档集进行预处理，提取出30095 个特征词

计算每篇文档中的特征词的TF*IDF值，实现文档向量化，在KNN算法中使用

用JAVA实现了KNN算法及朴素贝叶斯算法的newsgroup文本分类器

1、Newsgroup文档集介绍

Newsgroups最早由Lang于1995收集并在[Lang 1995]中使用。它含有20000篇左右的Usenet文档，几乎平均分配20个不同的新闻组。除了其中4.5%的文档属于两个或两个以上的新闻组以外，其余文档仅属于一个新闻组，因此它通常被作为单标注分类问题来处理。Newsgroups已经成为文本分及聚类中常用的文档集。美国MIT大学Jason Rennie对Newsgroups作了必要的处理，使得每个文档只属于一个新闻组，形成Newsgroups-18828。

2、Newsgroup文档预处理

要做文本分类首先得完成文本的预处理，预处理的主要步骤如下

STEP ONE:英文词法分析，去除数字、连字符、标点符号、特殊 字符，所有大写字母转换成小写，可以用正则表达式

                     String res[] = line.split("[^a-zA-Z]");

STEP TWO:去停用词，过滤对分类无价值的词

STEP THRE: 词根还原stemming,基于Porter算法

文档预处理类DataPreProcess.java如下

package com.pku.yangliu;import java.io.BufferedReader;import java.io.File;import java.io.FileReader;import java.io.FileWriter;import java.io.IOException;import java.util.ArrayList;/**  * Newsgroups文档集预处理类 */public class DataPreProcess {/**输入文件调用处理数据函数 * @param strDir newsgroup文件目录的绝对路径 * @throws IOException  */public void doProcess(String strDir) throws IOException{File fileDir = new File(strDir);if(!fileDir.exists()){System.out.println("File not exist:" + strDir);return;}String subStrDir = strDir.substring(strDir.lastIndexOf('/'));String dirTarget = strDir + "/../../processedSample_includeNotSpecial"+subStrDir;File fileTarget = new File(dirTarget);if(!fileTarget.exists()){//注意processedSample需要先建立目录建出来，否则会报错，因为母目录不存在fileTarget.mkdir();}File[] srcFiles = fileDir.listFiles();String[] stemFileNames = new String[srcFiles.length];for(int i = 0; i < srcFiles.length; i++){String fileFullName = srcFiles[i].getCanonicalPath();String fileShortName = srcFiles[i].getName();if(!new File(fileFullName).isDirectory()){//确认子文件名不是目录如果是可以再次递归调用System.out.println("Begin preprocess:"+fileFullName);StringBuilder stringBuilder = new StringBuilder();stringBuilder.append(dirTarget + "/" + fileShortName);createProcessFile(fileFullName, stringBuilder.toString());stemFileNames[i] = stringBuilder.toString();}else {fileFullName = fileFullName.replace("\\","/");doProcess(fileFullName);}}//下面调用stem算法if(stemFileNames.length > 0 && stemFileNames[0] != null){Stemmer.porterMain(stemFileNames);}}/**进行文本预处理生成目标文件 * @param srcDir 源文件文件目录的绝对路径 * @param targetDir 生成的目标文件的绝对路径 * @throws IOException  */private static void createProcessFile(String srcDir, String targetDir) throws IOException {// TODO Auto-generated method stubFileReader srcFileReader = new FileReader(srcDir);FileReader stopWordsReader = new FileReader("F:/DataMiningSample/stopwords.txt");FileWriter targetFileWriter = new FileWriter(targetDir);BufferedReader srcFileBR = new BufferedReader(srcFileReader);//装饰模式BufferedReader stopWordsBR = new BufferedReader(stopWordsReader);String line, resLine, stopWordsLine;//用stopWordsBR够着停用词的ArrayList容器ArrayList<String> stopWordsArray = new ArrayList<String>();while((stopWordsLine = stopWordsBR.readLine()) != null){if(!stopWordsLine.isEmpty()){stopWordsArray.add(stopWordsLine);}}while((line = srcFileBR.readLine()) != null){resLine = lineProcess(line,stopWordsArray);if(!resLine.isEmpty()){//按行写，一行写一个单词String[] tempStr = resLine.split(" ");//\sfor(int i = 0; i < tempStr.length; i++){if(!tempStr[i].isEmpty()){targetFileWriter.append(tempStr[i]+"\n");}}}}targetFileWriter.flush();targetFileWriter.close();srcFileReader.close();stopWordsReader.close();srcFileBR.close();stopWordsBR.close();}/**对每行字符串进行处理，主要是词法分析、去停用词和stemming * @param line 待处理的一行字符串 * @param ArrayList<String> 停用词数组 * @return String 处理好的一行字符串，是由处理好的单词重新生成，以空格为分隔符 * @throws IOException  */private static String lineProcess(String line, ArrayList<String> stopWordsArray) throws IOException {// TODO Auto-generated method stub//step1 英文词法分析，去除数字、连字符、标点符号、特殊字符，所有大写字母转换成小写，可以考虑用正则表达式String res[] = line.split("[^a-zA-Z]");//这里要小心，防止把有单词中间有数字和连字符的单词 截断了，但是截断也没事String resString = new String();//step2去停用词//step3stemming,返回后一起做for(int i = 0; i < res.length; i++){if(!res[i].isEmpty() && !stopWordsArray.contains(res[i].toLowerCase())){resString += " " + res[i].toLowerCase() + " ";}}return resString;}/** * @param args * @throws IOException  */public void BPPMain(String[] args) throws IOException {// TODO Auto-generated method stubDataPreProcess dataPrePro = new DataPreProcess();dataPrePro.doProcess("F:/DataMiningSample/orginSample");}}

steming的porter算法可以Google，有C及JAVA的实现版本，点击下载porter算法JAVA版本

2、特征词的选取

首先统计经过预处理后在所有文档中出现不重复的单词一共有87554个，对这些词进行统计发现：
出现次数大于等于1次的词有87554个
出现次数大于等于3次的词有36456个
出现次数大于等于4次的词有30095个
特征词的选取策略：
策略一：保留所有词作为特征词 共计87554个
策略二：选取出现次数大于等于4次的词作为特征词共计30095个
特征词的选取策略：采用策略一，后面将对两种特征词选取策略的计算时间和平均准确率做对比

测试集与训练集的创建类CreateTrainAndTestSample.java如下

package com.pku.yangliu;import java.io.BufferedReader;import java.io.File;import java.io.FileReader;import java.io.FileWriter;import java.io.IOException;import java.util.SortedMap;import java.util.TreeMap;/**创建训练样例集合与测试样例集合 * */public class CreateTrainAndTestSample {void filterSpecialWords() throws IOException {// TODO Auto-generated method stubString word;ComputeWordsVector cwv = new ComputeWordsVector();String fileDir = "F:/DataMiningSample/processedSample_includeNotSpecial";SortedMap<String,Double> wordMap = new TreeMap<String,Double>();wordMap = cwv.countWords(fileDir, wordMap);cwv.printWordMap(wordMap);//把wordMap输出到文件File[] sampleDir = new File(fileDir).listFiles();for(int i = 0; i < sampleDir.length; i++){File[] sample = sampleDir[i].listFiles();String targetDir = "F:/DataMiningSample/processedSampleOnlySpecial/"+sampleDir[i].getName();File targetDirFile = new File(targetDir);if(!targetDirFile.exists()){targetDirFile.mkdir();}for(int j = 0;j < sample.length; j++){String fileShortName = sample[j].getName();if(fileShortName.contains("stemed")){targetDir = "F:/DataMiningSample/processedSampleOnlySpecial/"+sampleDir[i].getName()+"/"+fileShortName.substring(0,5);FileWriter tgWriter= new FileWriter(targetDir);FileReader samReader = new FileReader(sample[j]);BufferedReader samBR = new BufferedReader(samReader);while((word = samBR.readLine()) != null){if(wordMap.containsKey(word)){tgWriter.append(word + "\n");}}tgWriter.flush();tgWriter.close();}}}}void createTestSamples(String fileDir, double trainSamplePercent,int indexOfSample,String classifyResultFile) throws IOException {// TODO Auto-generated method stubString word, targetDir;FileWriter crWriter = new FileWriter(classifyResultFile);//测试样例正确类目记录文件File[] sampleDir = new File(fileDir).listFiles();for(int i = 0; i < sampleDir.length; i++){File[] sample = sampleDir[i].listFiles();double testBeginIndex = indexOfSample*(sample.length * (1-trainSamplePercent));//测试样例的起始文件序号double testEndIndex = (indexOfSample+1)*(sample.length * (1-trainSamplePercent));//测试样例集的结束文件序号for(int j = 0;j < sample.length; j++){FileReader samReader = new FileReader(sample[j]);BufferedReader samBR = new BufferedReader(samReader);String fileShortName = sample[j].getName();String subFileName = fileShortName;if(j > testBeginIndex && j< testEndIndex){//序号在规定区间内的作为测试样本，需要为测试样本生成类别-序号文件，最后加入分类的结果，一行对应一个文件，方便统计准确率targetDir = "F:/DataMiningSample/TestSample"+indexOfSample+"/"+sampleDir[i].getName();crWriter.append(subFileName + " " + sampleDir[i].getName()+"\n");}else{//其余作为训练样本targetDir = "F:/DataMiningSample/TrainSample"+indexOfSample+"/"+sampleDir[i].getName();}targetDir = targetDir.replace("\\","/");File trainSamFile = new File(targetDir);if(!trainSamFile.exists()){trainSamFile.mkdir();}targetDir += "/"+subFileName;FileWriter tsWriter = new FileWriter(new File(targetDir));while((word = samBR.readLine()) != null){tsWriter.append(word + "\n");}tsWriter.flush();tsWriter.close();}}crWriter.flush();crWriter.close();}}

3、贝叶斯算法描述及实现

根据朴素贝叶斯公式，每个测试样例属于某个类别的概率 =  所有测试样例包含特征词类条件概率P(tk|c)之积 * 先验概率P(c)

在具体计算类条件概率和先验概率时，朴素贝叶斯分类器有两种模型

（1）多元分布模型( multinomial model )  –以单词为粒度，也就是说，考虑每个文件里面重复出现多次的单词。注意多项分布其实是从二项分布拓展出来的，如果采用多项分布模型，那么每个单词表示变量就不再是二值变量（出现/不出现），而是每个单词在文件中出现的次数
类条件概率P(tk|c)=(类c下单词tk在各个文档中出现过的次数之和+1)/（类c下单词总数+训练样本中不重复特征词总数）
先验概率P(c)=类c下的单词总数/整个训练样本的单词总数
（2）伯努利模型（Bernoulli model） –以文件为粒度，或者说是采用二项分布模型，伯努利实验即N次独立重复随机实验，只考虑事件发生/不发生，所以每个单词的表示变量是布尔型的
类条件概率P(tk|c)=（类c下包含单词tk的文件数+1）/(类c下文件总数+2)（注意：开始此处错写成了单词，多谢网友提醒后更正）
先验概率P(c)=类c下文件总数/整个训练样本的文件总数
本分类器选用多元分布模型计算，根据《Introduction to Information Retrieval 》，多元分布模型计算准确率更高

贝叶斯算法的实现有以下注意点：

(1) 计算概率用到了BigDecimal类实现任意精度计算
(2) 用交叉验证法做十次分类实验，对准确率取平均值
(3) 根据正确类目文件和分类结果文计算混淆矩阵并且输出
(4) Map<String,Double> cateWordsProb key为“类目_单词”, value为该类目下该单词的出现次数，避免重复计算

贝叶斯算法实现类如下 NaiveBayesianClassifier.java

package com.pku.yangliu;import java.io.BufferedReader;import java.io.File;import java.io.FileReader;import java.io.FileWriter;import java.io.IOException;import java.math.BigDecimal;import java.util.Iterator;import java.util.Map;import java.util.Set;import java.util.SortedSet;import java.util.TreeMap;import java.util.TreeSet;import java.util.Vector;/**利用朴素贝叶斯算法对newsgroup文档集做分类，采用十组交叉测试取平均值 * 采用多项式模型,stanford信息检索导论课件上面言多项式模型比伯努利模型准确度高 * 类条件概率P(tk|c)=(类c 下单词tk 在各个文档中出现过的次数之和+1)/(类c下单词总数+|V|) * */public class NaiveBayesianClassifier {/**用贝叶斯法对测试文档集分类 * @param trainDir 训练文档集目录 * @param testDir 测试文档集目录 * @param classifyResultFileNew 分类结果文件路径 * @throws Exception  */private void doProcess(String trainDir, String testDir,String classifyResultFileNew) throws Exception {// TODO Auto-generated method stubMap<String,Double> cateWordsNum = new TreeMap<String,Double>();//保存训练集每个类别的总词数Map<String,Double> cateWordsProb = new TreeMap<String,Double>();//保存训练样本每个类别中每个属性词的出现词数cateWordsProb = getCateWordsProb(trainDir);cateWordsNum = getCateWordsNum(trainDir);double totalWordsNum = 0.0;//记录所有训练集的总词数Set<Map.Entry<String,Double>> cateWordsNumSet = cateWordsNum.entrySet();for(Iterator<Map.Entry<String,Double>> it = cateWordsNumSet.iterator(); it.hasNext();){Map.Entry<String, Double> me = it.next();totalWordsNum += me.getValue();}//下面开始读取测试样例做分类Vector<String> testFileWords = new Vector<String>();String word;File[] testDirFiles = new File(testDir).listFiles();FileWriter crWriter = new FileWriter(classifyResultFileNew);for(int i = 0; i < testDirFiles.length; i++){File[] testSample = testDirFiles[i].listFiles();for(int j = 0;j < testSample.length; j++){testFileWords.clear();FileReader spReader = new FileReader(testSample[j]);BufferedReader spBR = new BufferedReader(spReader);while((word = spBR.readLine()) != null){testFileWords.add(word);}//下面分别计算该测试样例属于二十个类别的概率File[] trainDirFiles = new File(trainDir).listFiles();BigDecimal maxP = new BigDecimal(0);String bestCate = null;for(int k = 0; k < trainDirFiles.length; k++){BigDecimal p = computeCateProb(trainDirFiles[k], testFileWords, cateWordsNum, totalWordsNum, cateWordsProb);if(k == 0){maxP = p;bestCate = trainDirFiles[k].getName();continue;}if(p.compareTo(maxP) == 1){maxP = p;bestCate = trainDirFiles[k].getName();}}crWriter.append(testSample[j].getName() + " " + bestCate + "\n");crWriter.flush();}}crWriter.close();}/**统计某类训练样本中每个单词的出现次数 * @param strDir 训练样本集目录 * @return Map<String,Double> cateWordsProb 用"类目_单词"对来索引的map,保存的val就是该类目下该单词的出现次数 * @throws IOException  */public Map<String,Double> getCateWordsProb(String strDir) throws IOException{Map<String,Double> cateWordsProb = new TreeMap<String,Double>();File sampleFile = new File(strDir);File [] sampleDir = sampleFile.listFiles();String word;for(int i = 0;i < sampleDir.length; i++){File [] sample = sampleDir[i].listFiles();for(int j = 0; j < sample.length; j++){FileReader samReader = new FileReader(sample[j]);BufferedReader samBR = new BufferedReader(samReader);while((word = samBR.readLine()) != null){String key = sampleDir[i].getName() + "_" + word;if(cateWordsProb.containsKey(key)){double count = cateWordsProb.get(key) + 1.0;cateWordsProb.put(key, count);}else {cateWordsProb.put(key, 1.0);}}}}return cateWordsProb;}/**计算某一个测试样本属于某个类别的概率 * @param Map<String, Double> cateWordsProb 记录每个目录中出现的单词及次数  * @param File trainFile 该类别所有的训练样本所在目录 * @param Vector<String> testFileWords 该测试样本中的所有词构成的容器 * @param double totalWordsNum 记录所有训练样本的单词总数 * @param Map<String, Double> cateWordsNum 记录每个类别的单词总数 * @return BigDecimal 返回该测试样本在该类别中的概率 * @throws Exception  * @throws IOException  */private BigDecimal computeCateProb(File trainFile, Vector<String> testFileWords, Map<String, Double> cateWordsNum, double totalWordsNum, Map<String, Double> cateWordsProb) throws Exception {// TODO Auto-generated method stubBigDecimal probability = new BigDecimal(1);double wordNumInCate = cateWordsNum.get(trainFile.getName());BigDecimal wordNumInCateBD = new BigDecimal(wordNumInCate);BigDecimal totalWordsNumBD = new BigDecimal(totalWordsNum);for(Iterator<String> it = testFileWords.iterator(); it.hasNext();){String me = it.next();String key = trainFile.getName()+"_"+me;double testFileWordNumInCate;if(cateWordsProb.containsKey(key)){testFileWordNumInCate = cateWordsProb.get(key);}else testFileWordNumInCate = 0.0;BigDecimal testFileWordNumInCateBD = new BigDecimal(testFileWordNumInCate);BigDecimal xcProb = (testFileWordNumInCateBD.add(new BigDecimal(0.0001))).divide(totalWordsNumBD.add(wordNumInCateBD),10, BigDecimal.ROUND_CEILING);probability = probability.multiply(xcProb);}BigDecimal res = probability.multiply(wordNumInCateBD.divide(totalWordsNumBD,10, BigDecimal.ROUND_CEILING));return res;}/**获得每个类目下的单词总数 * @param trainDir 训练文档集目录 * @return Map<String, Double> <目录名，单词总数>的map * @throws IOException  */private Map<String, Double> getCateWordsNum(String trainDir) throws IOException {// TODO Auto-generated method stubMap<String,Double> cateWordsNum = new TreeMap<String,Double>();File[] sampleDir = new File(trainDir).listFiles();for(int i = 0; i < sampleDir.length; i++){double count = 0;File[] sample = sampleDir[i].listFiles();for(int j = 0;j < sample.length; j++){FileReader spReader = new FileReader(sample[j]);BufferedReader spBR = new BufferedReader(spReader);while(spBR.readLine() != null){count++;}}cateWordsNum.put(sampleDir[i].getName(), count);}return cateWordsNum;}/**根据正确类目文件和分类结果文件统计出准确率 * @param classifyResultFile 正确类目文件 * @param classifyResultFileNew 分类结果文件 * @return double 分类的准确率 * @throws IOException  */double computeAccuracy(String classifyResultFile,String classifyResultFileNew) throws IOException {// TODO Auto-generated method stubMap<String,String> rightCate = new TreeMap<String,String>();Map<String,String> resultCate = new TreeMap<String,String>();rightCate = getMapFromResultFile(classifyResultFile);resultCate = getMapFromResultFile(classifyResultFileNew);Set<Map.Entry<String, String>> resCateSet = resultCate.entrySet();double rightCount = 0.0;for(Iterator<Map.Entry<String, String>> it = resCateSet.iterator(); it.hasNext();){Map.Entry<String, String> me = it.next();if(me.getValue().equals(rightCate.get(me.getKey()))){rightCount ++;}}computerConfusionMatrix(rightCate,resultCate);return rightCount / resultCate.size();}/**根据正确类目文件和分类结果文计算混淆矩阵并且输出 * @param rightCate 正确类目对应map * @param resultCate 分类结果对应map * @return double 分类的准确率 * @throws IOException  */private void computerConfusionMatrix(Map<String, String> rightCate,Map<String, String> resultCate) {// TODO Auto-generated method stubint[][] confusionMatrix = new int[20][20];//首先求出类目对应的数组索引SortedSet<String> cateNames = new TreeSet<String>();Set<Map.Entry<String, String>> rightCateSet = rightCate.entrySet();for(Iterator<Map.Entry<String, String>> it = rightCateSet.iterator(); it.hasNext();){Map.Entry<String, String> me = it.next();cateNames.add(me.getValue());}cateNames.add("rec.sport.baseball");//防止数少一个类目String[] cateNamesArray = cateNames.toArray(new String[0]);Map<String,Integer> cateNamesToIndex = new TreeMap<String,Integer>();for(int i = 0; i < cateNamesArray.length; i++){cateNamesToIndex.put(cateNamesArray[i],i);}for(Iterator<Map.Entry<String, String>> it = rightCateSet.iterator(); it.hasNext();){Map.Entry<String, String> me = it.next();confusionMatrix[cateNamesToIndex.get(me.getValue())][cateNamesToIndex.get(resultCate.get(me.getKey()))]++;}//输出混淆矩阵double[] hangSum = new double[20];System.out.print("    ");for(int i = 0; i < 20; i++){System.out.print(i + "    ");}System.out.println();for(int i = 0; i < 20; i++){System.out.print(i + "    ");for(int j = 0; j < 20; j++){System.out.print(confusionMatrix[i][j]+"    ");hangSum[i] += confusionMatrix[i][j];}System.out.println(confusionMatrix[i][i] / hangSum[i]);}System.out.println();}/**从分类结果文件中读取map * @param classifyResultFileNew 类目文件 * @return Map<String, String> 由<文件名，类目名>保存的map * @throws IOException  */private Map<String, String> getMapFromResultFile(String classifyResultFileNew) throws IOException {// TODO Auto-generated method stubFile crFile = new File(classifyResultFileNew);FileReader crReader = new FileReader(crFile);BufferedReader crBR = new BufferedReader(crReader);Map<String, String> res = new TreeMap<String, String>();String[] s;String line;while((line = crBR.readLine()) != null){s = line.split(" ");res.put(s[0], s[1]);}return res;}/** * @param args * @throws Exception  */public void NaiveBayesianClassifierMain(String[] args) throws Exception { //TODO Auto-generated method stub//首先创建训练集和测试集CreateTrainAndTestSample ctt = new CreateTrainAndTestSample();NaiveBayesianClassifier nbClassifier = new NaiveBayesianClassifier();ctt.filterSpecialWords();//根据包含非特征词的文档集生成只包含特征词的文档集到processedSampleOnlySpecial目录下double[] accuracyOfEveryExp = new double[10];double accuracyAvg,sum = 0;for(int i = 0; i < 10; i++){//用交叉验证法做十次分类实验，对准确率取平均值String TrainDir = "F:/DataMiningSample/TrainSample"+i;String TestDir = "F:/DataMiningSample/TestSample"+i;String classifyRightCate = "F:/DataMiningSample/classifyRightCate"+i+".txt";String classifyResultFileNew = "F:/DataMiningSample/classifyResultNew"+i+".txt";ctt.createTestSamples("F:/DataMiningSample/processedSampleOnlySpecial", 0.9, i,classifyRightCate);nbClassifier.doProcess(TrainDir,TestDir,classifyResultFileNew);accuracyOfEveryExp[i] = nbClassifier.computeAccuracy (classifyRightCate, classifyResultFileNew);System.out.println("The accuracy for Naive Bayesian Classifier in "+i+"th Exp is :" + accuracyOfEveryExp[i]);}for(int i = 0; i < 10; i++){sum += accuracyOfEveryExp[i];}accuracyAvg = sum / 10;System.out.println("The average accuracy for Naive Bayesian Classifier in all Exps is :" + accuracyAvg);}}

4 朴素贝叶斯算法对newsgroup文档集做分类的结果

为方便计算混淆矩阵，将类目编号如下

0 alt.atheism
1 comp.graphics
2 comp.os.ms-windows.misc
3comp.sys.ibm.pc.hdwar
4comp.sys.mac.hardwar
5 comp.windows.x
6 misc.forsale
7 rec.autos
8 rec.motorcycles
9 rec.sport.baseball
10 rec.sport.hockey
11 sci.crypt
12 sci.electronics
13 sci.med
14 sci.space
15 soc.religion.christian
16 talk.politics.guns
17 talk.politics.mideast
18 talk.politics.misc
19 talk.religion.misc

贝叶斯算法分类结果-混淆矩阵表示，以交叉验证的第6次实验结果为例，分类准确率达到80.47%

程序运行硬件环境：Intel Core 2 Duo CPU T5750 2GHZ, 2G内存，实验结果如下
取所有词共87554个作为特征词：10次交叉验证实验平均准确率78.19%，用时23min,准确率范围75.65%-80.47%，第6次实验准确率超过80%
取出现次数大于等于4次的词共计30095个作为特征词： 10次交叉验证实验平均准确率77.91%，用时22min，准确率范围75.51%-80.26%，第6次实验准确率超过80%

结论：朴素贝叶斯算法不必去除出现次数很低的词，因为出现次数很低的词的IDF比较   大，去除后分类准确率下降，而计算时间并没有显著减少

5 贝叶斯算法的改进

为了进一步提高贝叶斯算法的分类准确率，可以考虑

(1) 优化特征词的选取策略

(2)改进多项式模型的类条件概率的计算公式，改进为 类条件概率P(tk|c)=(类c下单词tk在各个文档中出现过的次数之和+0.001)/（类c下单词总数+训练样本中不重复特征词总数），分子当tk没有出现时，只加0.001，这样更加精确的描述的词的统计分布规律，做此改进后的混淆矩阵如下

可以看到第6次分组实验的准确率提高到84.79%，第7词分组实验的准确率达到85.24%，平均准确率由77.91%提高到了82.23%,优化效果还是很明显的

KNN算法描述及JAVA实现，和两种算法的准确率对比，见数据挖掘-基于贝叶斯算法及KNN算法的newsgroup18828文档分类器的JAVA实现（下）