在将Weka GUI分类结果与我的Java程序进行比较时，使用iris数据集执行树（J48）时，我遇到了一些问题。如果你能帮助我，我将非常感激。
我正在使用iris数据集，我正在尝试开发一个Java程序来对新实例进行分类。为此，我使用Weka GUI获得了一个模型（＆＃34; iris_tree（CV）.model＆＃34;），该模型经过培训和验证（交叉验证10次）。 Weka GUI的结果是好的和预期的：4个错误分类的实例。之后我保存模型以供我的Java程序稍后使用。
当我加载模型＆＃34; iris_tree（CV）.model＆＃34;在我的Java程序中，我尝试对新实例（测试数据集）进行分类，结果是不同的：Java程序很好地分类＆＃39; setosa＆＃39;和＆＃39; virginica＆＃39;，但不是＆＃39; versicolour＆＃39;。结果如下：
Classification: setosa
Classification: setosa
Classification: virginica
Classification: virginica
Classification: virginica
Classification: virginica
当我希望获得：
Classification: setosa
Classification: setosa
Classification: versicolour
Classification: versicolour
Classification: virginica
Classification: virginica
我已经阅读了一些相关帖子，但在使用Java而不是Weka GUI时，我无法找到对这种奇怪行为的明确回应。
我将Java代码附加在2个类中，稍后是训练和测试集。提前谢谢。
主要班级：
public static void main(String[] args) {
try {
Hashtable<String, String> values = new Hashtable<String, String>();
//Loading the model
String pathModel="";
String pathTestSet="";
JFileChooser chooserModel = new JFileChooser();
chooserModel.setCurrentDirectory(new java.io.File("."));
chooserModel.setDialogTitle("HoliDes: choose the model");
chooserModel.setFileSelectionMode(JFileChooser.FILES_AND_DIRECTORIES);
chooserModel.setAcceptAllFileFilterUsed(true);
if (chooserModel.showOpenDialog(null) == JFileChooser.APPROVE_OPTION) {
File filePathModel=chooserModel.getSelectedFile();
pathModel=filePathModel.getPath();
State irisModel = new State(pathModel);
//Loading the model
JFileChooser chooserTestSet = new JFileChooser();
chooserTestSet.setDialogTitle("HoliDes: choose TEST SET");
chooserTestSet.setFileSelectionMode(JFileChooser.FILES_AND_DIRECTORIES);
chooserTestSet.setAcceptAllFileFilterUsed(true);
//Loading the testing dataset
if (chooserTestSet.showOpenDialog(null) == JFileChooser.APPROVE_OPTION) {
File filePathTestSet=chooserTestSet.getSelectedFile();
pathTestSet=filePathTestSet.getPath();
//Transforming the data set into pairs attribute-value
ConverterUtils.DataSource unlabeledSource = new ConverterUtils.DataSource(pathTestSet);
Instances unlabeledData = unlabeledSource.getDataSet();
if (unlabeledData.classIndex() == -1){
unlabeledData.setClassIndex(unlabeledData.numAttributes() - 1);
}
for (int i = 0; i < unlabeledData.numInstances(); i++) {
Instance ins=unlabeledData.instance(i);
for (int j = 0; j < ins.numAttributes(); j++) {
String attrib=ins.attribute(j).name();
double val=ins.value(ins.attribute(j));
values.put(attrib,String.valueOf(val));
}
System.out.println("Classification: " + irisModel.classifySpecies(values,pathModel));
}
}
}
} catch (Exception ex) {
Logger.getLogger(PilotPatternClassifier.class.getName()).log(Level.SEVERE, null, ex);
}
}
国家级：
public class State {
//private String classModelFile = "/iris_tree.model";
private Classifier classModel;
private Instances dataModel;
/**
* Class constructor.
*/
public State(String pathModel) throws Exception {
//InputStream classModelStream;
// Create a stream object for the model file embedded within the JAR file.
//classModelStream = getClass().getResourceAsStream(classModelFile);
classModel=(Classifier) weka.core.SerializationHelper.read(pathModel);
}
/**
* Close the instance by setting both the model file string and
* the model object itself to null. When the garbage collector
* runs, this should make clean up simpler. However, the garbage
* collector is not called synchronously since that should be
* managed by the larger execution environment.
*/
public void close() {
classModel = null;
//classModelFile=null;
}
/**
* Evaluate the model on the data provided by @param measures.
* This returns a string with the species name.
*
* @param measures object with petal and sepal measurements
* @return string with the species name
* @throws Exception
*/
public String classifySpecies(Dictionary<String, String> measures, String pathTestSet) throws Exception {
FastVector dataClasses = new FastVector();
FastVector dataAttribs = new FastVector();
Attribute species;
double values[] = new double[measures.size() + 1];
int i = 0, maxIndex = 0;
// Assemble the potential species options.
dataClasses.addElement("setosa");
dataClasses.addElement("versicolour");
dataClasses.addElement("virginica");
species = new Attribute("species", dataClasses);
// Create the object to classify on.
for (Enumeration<String> keys = measures.keys(); keys.hasMoreElements(); ) {
String key = keys.nextElement();
double val = Double.parseDouble(measures.get(key));
dataAttribs.addElement(new Attribute(key));
values[i++] = val;
}
dataAttribs.addElement(species);
dataModel = new Instances("iris-test", dataAttribs, 0);//"classify" is the name of the relationship of the test file. It is arbitrary
dataModel.setClass(species);
Instance ins=new DenseInstance(1, values);
//dataModel.add(new Instance(1, values) {});
dataModel.add(ins);
dataModel.instance(0).setClassMissing();
// Find the class with the highest estimated likelihood
double cl[] = classModel.distributionForInstance(dataModel.instance(0));
for(i = 0; i < cl.length; i++){
if(cl[i] > cl[maxIndex]){
maxIndex = i;
}
}
return dataModel.classAttribute().value(maxIndex);
}
}
这里是培训和测试集：
@RELATION iris-train
@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth REAL
@ATTRIBUTE petallength REAL
@ATTRIBUTE petalwidth REAL
@ATTRIBUTE species {setosa,versicolour,virginica}
@DATA
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5.0,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3.0,1.4,0.1,setosa
4.3,3.0,1.1,0.1,setosa
5.8,4.0,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1.0,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5.0,3.0,1.6,0.2,setosa
5.0,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.0,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.1,1.5,0.1,setosa
4.4,3.0,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5.0,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5.0,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3.0,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5.0,3.3,1.4,0.2,setosa
7.0,3.2,4.7,1.4,versicolour
6.4,3.2,4.5,1.5,versicolour
6.9,3.1,4.9,1.5,versicolour
5.5,2.3,4.0,1.3,versicolour
6.5,2.8,4.6,1.5,versicolour
5.7,2.8,4.5,1.3,versicolour
6.3,3.3,4.7,1.6,versicolour
4.9,2.4,3.3,1.0,versicolour
6.6,2.9,4.6,1.3,versicolour
5.2,2.7,3.9,1.4,versicolour
5.0,2.0,3.5,1.0,versicolour
5.9,3.0,4.2,1.5,versicolour
6.0,2.2,4.0,1.0,versicolour
6.1,2.9,4.7,1.4,versicolour
5.6,2.9,3.6,1.3,versicolour
6.7,3.1,4.4,1.4,versicolour
5.6,3.0,4.5,1.5,versicolour
5.8,2.7,4.1,1.0,versicolour
6.2,2.2,4.5,1.5,versicolour
5.6,2.5,3.9,1.1,versicolour
5.9,3.2,4.8,1.8,versicolour
6.1,2.8,4.0,1.3,versicolour
6.3,2.5,4.9,1.5,versicolour
6.1,2.8,4.7,1.2,versicolour
6.4,2.9,4.3,1.3,versicolour
6.6,3.0,4.4,1.4,versicolour
6.8,2.8,4.8,1.4,versicolour
6.7,3.0,5.0,1.7,versicolour
6.0,2.9,4.5,1.5,versicolour
5.7,2.6,3.5,1.0,versicolour
5.5,2.4,3.8,1.1,versicolour
5.5,2.4,3.7,1.0,versicolour
5.8,2.7,3.9,1.2,versicolour
6.0,2.7,5.1,1.6,versicolour
5.4,3.0,4.5,1.5,versicolour
6.0,3.4,4.5,1.6,versicolour
6.7,3.1,4.7,1.5,versicolour
6.3,2.3,4.4,1.3,versicolour
5.6,3.0,4.1,1.3,versicolour
5.5,2.5,4.0,1.3,versicolour
5.5,2.6,4.4,1.2,versicolour
6.1,3.0,4.6,1.4,versicolour
5.8,2.6,4.0,1.2,versicolour
5.0,2.3,3.3,1.0,versicolour
5.6,2.7,4.2,1.3,versicolour
5.7,3.0,4.2,1.2,versicolour
5.7,2.9,4.2,1.3,versicolour
6.2,2.9,4.3,1.3,versicolour
5.1,2.5,3.0,1.1,versicolour
5.7,2.8,4.1,1.3,versicolour
6.3,3.3,6.0,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3.0,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3.0,5.8,2.2,virginica
7.6,3.0,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2.0,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3.0,5.5,2.1,virginica
5.7,2.5,5.0,2.0,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3.0,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6.0,2.2,5.0,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2.0,virginica
7.7,2.8,6.7,2.0,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6.0,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3.0,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3.0,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2.0,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3.0,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6.0,3.0,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3.0,5.2,2.3,virginica
6.3,2.5,5.0,1.9,virginica
6.5,3.0,5.2,2.0,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3.0,5.1,1.8,virginica
和
@RELATION iris-test
@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth REAL
@ATTRIBUTE petallength REAL
@ATTRIBUTE petalwidth REAL
@DATA
5.1,3.5,1.4,0.2
4.9,3.0,1.4,0.2
6.6,3.0,4.4,1.4
6.8,2.8,4.8,1.4
6.4,3.1,5.5,1.8
6.0,3.0,4.8,1.8
非常感谢你的帮助。
答案 0 :(得分：0)
我认为将分类器模型应用于测试集时的准确度低于使用训练集功能文件检查时的准确性。尝试使用Weka GUI和此测试集，也许您将获得相同的结果。这不是GUI与Java的问题
我会将此作为评论，但由于缺乏声誉而无法发表评论。