决策树分类器在分类实例时遇到问题

Question

我正在进行特征选择，我想使用决策树（DT）分类器作为结果的评估者。我遇到的问题是，DT无法用名义值对测试实例进行分类。这会使代码引发以下异常

Exception in thread "main" net.sf.javaml.tools.weka.WekaException: java.lang.IllegalArgumentException: Value not defined for given nominal attribute!
    at net.sf.javaml.tools.weka.WekaClassifier.classify(WekaClassifier.java:43)
    at psofc2.MyClassifier.classify(MyClassifier.java:101)
    at psofc2.TestingWithClassifier.fc(TestingWithClassifier.java:137)
    at psofc2.Main.main(Main.java:399)

我需要知道这是怎么发生的，同时我为类标签有两个标称值。引起主要问题的与第99行相关的代码是

Object prediction = getClassifier().classify(instance);

与以下方法有关

public double classify(Dataset training, Dataset testing) {
    getClassifier().buildClassifier(training);
    Dataset dataForClassification = testing;
    Map<Object, PerformanceMeasure> out = new HashMap<Object, PerformanceMeasure>();
    for (Object o : training.classes()) {
        out.put(o, new PerformanceMeasure());
    }
    for (Instance instance : dataForClassification) {

        Object prediction = getClassifier().classify(instance);
        if (prediction!=null){
        if (instance.classValue().equals(prediction)) 
            for (Object o : out.keySet()) {
                if (o.equals(instance.classValue())) {
                    out.get(o).tp++;
                } else {
                    out.get(o).tn++;
                }
            }
        } else {
            for (Object o : out.keySet()) {
                /* prediction is positive class */
                if (prediction.equals(o)) {
                    out.get(o).fp++;
                } /* instance is positive class */ else if (o.equals(instance.classValue())) {
                    out.get(o).fn++;
                } /* none is positive class */ else {
                    out.get(o).tn++;
                }
            }
        }
    }
    }
    double tp = 0.0, tn = 0.0;
    double fp = 0.0, fn = 0.0;
    double Accuracy = 0.0;
    for (Object o : out.keySet()) {
        tp += out.get(o).tp;
        tn += out.get(o).tn;
        fp += out.get(o).fp;
        fn += out.get(o).fn;
    }
   Accuracy = (tn + tp) / (double) (out.size() * dataForClassification.size());
           return Accuracy;
}

我的数据示例

@relation colon

@attribute f1 numeric
@attribute f2 numeric
@attribute f3 numeric
@attribute f4 numeric
@attribute f5 numeric
@attribute f6 numeric
@attribute f7 numeric
@attribute f8 numeric
@attribute f9 numeric
@attribute class {1,2}

@data
-2,-2,-2,-2,0,-2,0,-2,0,2
-2,-2,0,0,0,2,0,0,0,1
2,2,0,-2,-2,2,2,-2,0,2
-2,-2,-2,-2,-2,2,0,2,0,1