创建和分类新实例 Weka

Question

我使用 Weka API 和 Java 创建了决策树 (J48)。首先，我使用 arff 文件训练我的决策树。

public static void Tree(String Path) throws Exception {//Path path for the arff file
    J48 tree = new J48();         // new instance of tree
    DataSource source = new DataSource(Path);
    Instances data = source.getDataSet();
    // setting class attribute if the data format does not provide this information
    // For example, the XRFF format saves the class attribute information as well
    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }
    tree.buildClassifier(data);
    System.out.println(tree.toString());
}

使用的arff文件包括780个实例。每个实例都有 6 个属性 {PT1, w1, d1, PT2, w2, d2} 都是数字和一个 class {yes, no}。我的代码正在运行，我可以使用

查看生成的决策树

System.out.println(tree.toString());

现在，我想创建一个新实例（不使用另一个 arff 文件）并class验证这个新实例。例如，假设这个新实例的值是 {50, 5, 800, 74, 3, 760}。那么Decision Tree一定return对应的class（一个"yes"或者一个"no"）。

Answer 1

我找到了解决我问题的方法，希望它有用。

    //Declaring attributes
    Attribute PT1 = new Attribute("PT1");
    Attribute w1 = new Attribute("w1");
    Attribute d1 = new Attribute("d1");
    Attribute PT2 = new Attribute("PT2");
    Attribute w2 = new Attribute("w2");
    Attribute d2 = new Attribute("d2");

    // Declare the class attribute along with its values contains two nominal values yes and no using FastVector. "ScheduledFirst" is the name of the class attribute        
    FastVector fvClassVal = new FastVector(2);
    fvClassVal.addElement("yes");
    fvClassVal.addElement("no");
    Attribute Class = new Attribute("ScheduledFirst", fvClassVal);

    // Declare the feature vector
    FastVector fvWekaAttributes = new FastVector(7);
    // Add attributes 
    fvWekaAttributes.addElement(PT1);
    fvWekaAttributes.addElement(w1);
    fvWekaAttributes.addElement(d1);
    fvWekaAttributes.addElement(PT2);
    fvWekaAttributes.addElement(w2);
    fvWekaAttributes.addElement(d2);
    fvWekaAttributes.addElement(Class);
    // Declare Instances which is required since I want to use classification/Prediction
    Instances dataset = new Instances("whatever", fvWekaAttributes, 0);

    //Creating a double array and defining values
    double[] attValues = new double[dataset.numAttributes()];
    attValues[0] = 50;
    attValues[1] = 5;
    attValues[2] = 800;
    attValues[3] = 74;
    attValues[4] = 3;
    attValues[5] = 760;

    //Create the new instance i1
    Instance i1 = new Instance(1.0, attValues);
    //Add the instance to the dataset (Instances) (first element 0)        
    dataset.add(i1);
    //Define class attribute position
    dataset.setClassIndex(dataset.numAttributes()-1);

    //Will print 0 if it's a "yes", and 1 if it's a "no"
    System.out.println(tree.classifyInstance(dataset.instance(0)));
    //Here I call dataset.instance(0) since there is only one instance added in the dataset, if you do add another one you can use dataset.instance(0), etc.

创建和分类新实例 Weka

Create and classify new instance Weka

java

weka