1 介绍：嵌入式机器学习，在自己的算法中调用Weka现文本分类,是一个小的数据挖掘程序，虽然实用价值不是很大，但对于Weka的理解和使用是有帮助的。本例子来自《数据挖掘：实用机器学习技术》第2版（好像是倒数第三章）。大家可以到http://blogger.org.cn/blog/message.asp?name=DMman#23691 下载该书察看对算法的详细解释。算法中作了详细的注释，虽然是英文的，但还是比较简单。下面对例子的使用作了浅显的介绍，有兴趣的朋友可以研究。
2 功能：使用weka中的j48分类器实现了文本分类的一个小程序。文本文件通过weka的过滤器StringToWordVector预处理。
3 注意：把weka.jar加入你的classpath中，才可以通过编译。
4 使用方法：命令行参数： -t 文本文件路径 -m 你的模型文件路径 -c 可选，类别（hit 或 miss）如果提供了-c则用于训练，否则被模型分类，输出该文本的类型（hit或miss）
模型是动态建立的，第一次使用命令行必须指定-c参数，才可以建立模型。1) 建立模型>java MessageClassifier -t data/1.bmp -m myModel -c hit可以看到myModel建立了。然后继续训练一下这个模型。使用的文本实例越多，模型的分类性能越好>java MessageClassifier -t data/2.bmp -m myModel -c hit>java MessageClassifier -t data/1.gif -m myModel -c miss......2) 使用模型分类有了模型，就可以使用它为文本文件分类了，如>java MessageClassifier -t data/2.gif -m myModel 3) 可以使用提供-c参数的命令继续完善模型
原文件MessageClassifier .java
/*** Java program for classifying text messages into two classes.*/import weka.core.Attribute;import weka.core.Instance;import weka.core.Instances;import weka.core.FastVector;import weka.core.Utils;import weka.classifiers.Classifier;import weka.classifiers.trees.J48;import weka.filters.Filter;import weka.filters.unsupervised.attribute.StringToWordVector;import java.io.*;public class MessageClassifier implements Serializable {/* The training data gathered so far. */private Instances m_Data = null;/* The filter used to generate the word counts. */private StringToWordVector m_Filter = new StringToWordVector();/* The actual classifier. */private Classifier m_Classifier = new J48();/* Whether the model is up to date. */private boolean m_UpToDate;/*** Constructs empty training dataset.*/public MessageClassifier() throws Exception {String nameOfDataset = "MessageClassificationProblem";// Create vector of attributes.FastVector attributes = new FastVector(2);// Add attribute for holding messages.attributes.addElement(new Attribute("Message", (FastVector)null));// Add class attribute.FastVector classValues = new FastVector(2);classValues.addElement("miss");classValues.addElement("hit");attributes.addElement(new Attribute("Class", classValues));// Create dataset with initial capacity of 100, and set index of class.m_Data = new Instances(nameOfDataset, attributes, 100);m_Data.setClassIndex(m_Data.numAttributes() - 1);}/*** Updates data using the given training message.*/public void updateData(String message, String classValue) throws Exception {// Make message into instance.Instance instance = makeInstance(message, m_Data);// Set class value for instance.instance.setClassValue(classValue);// Add instance to training data.m_Data.add(instance);m_UpToDate = false;}/*** Classifies a given message.*/public void classifyMessage(String message) throws Exception {// Check whether classifier has been built.if (m_Data.numInstances() == 0) {////throw new Exception("No classifier available.");}// Check whether classifier and filter are up to date.if (!m_UpToDate) { // Initialize filter and tell it about the input format.m_Filter.setInputFormat(m_Data);// Generate word counts from the training data.Instances filteredData = Filter.useFilter(m_Data, m_Filter);// Rebuild classifier.m_Classifier.buildClassifier(filteredData);m_UpToDate = true;}// Make separate little test set so that message// does not get added to string attribute in m_Data.Instances testset = m_Data.stringFreeStructure();// Make message into test instance.Instance instance = makeInstance(message, testset);// Filter instance.m_Filter.input(instance);Instance filteredInstance = m_Filter.output();// Get index of predicted class value.double predicted = m_Classifier.classifyInstance(filteredInstance);// Output class value.System.err.println("Message classified as : " +m_Data.classAttribute().value((int)predicted));}/*** Method that converts a text message into an instance.*/private Instance makeInstance(String text, Instances data) {// Create instance of length two.Instance instance = new Instance(2);// Set value for message attributeAttribute messageAtt = data.attribute("Message");instance.setValue(messageAtt, messageAtt.addStringValue(text));// Give instance access to attribute information from the dataset.instance.setDataset(data);return instance;}/*** Main method.*/public static void main(String[] options) {try {// Read message file into string.String messageName = Utils.getOption('t', options);if (messageName.length() == 0) {throw new Exception("Must provide name of message file.");}FileReader m = new FileReader(messageName);StringBuffer message = new StringBuffer(); int l;while ((l = m.read()) != -1) {message.append((char)l);}m.close();// Check if class value is given.String classValue = Utils.getOption('c', options);// If model file exists, read it, otherwise create new one.String modelName = Utils.getOption('m', options);if (modelName.length() == 0) {throw new Exception("Must provide name of model file.");}MessageClassifier messageCl;try {ObjectInputStream modelInObjectFile =new ObjectInputStream(new FileInputStream(modelName));messageCl = (MessageClassifier) modelInObjectFile.readObject();modelInObjectFile.close();} catch (FileNotFoundException e) {messageCl = new MessageClassifier();}// Check if there are any options leftUtils.checkForRemainingOptions(options);// Process message.if (classValue.length() != 0) {messageCl.updateData(message.toString(), classValue);} else {messageCl.classifyMessage(message.toString());}// Save message classifier object.ObjectOutputStream modelOutObjectFile =new ObjectOutputStream(new FileOutputStream(modelName));modelOutObjectFile.writeObject(messageCl);modelOutObjectFile.close();} catch (Exception e) {e.printStackTrace();}}}
下载源码:500)this.width=500'>文本分类算法.rar
