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我是java和Weka工具的初學者，我想用DecisionStump中的Logitboost算法作爲我的java代碼中的弱學習器，但我不知道這是如何工作的。我創建了一個具有六個特徵（無標籤特徵）的矢量，並且我想將它饋送到logitboost以便標記和分配它的概率。標籤是1或-1，列車/測試數據是在一個變量文件中。這是我的代碼，但算法總是返回0！ 謝謝在Weka中標記未標記的實例（java代碼）
double candidate_similarity(ha_nodes ha , WeightMatrix[][] wm , LogitBoost lgb ,ArrayList<Attribute> atts){
LogitBoost lgb = new LogitBoost();
lgb.buildClassifier(newdata);//newdata is an arff file with some labeled data
Evaluation eval = new Evaluation(newdata);
eval.crossValidateModel(lgb, newdata, 10, new Random(1));
try {
feature_vector[0] = IP_sim(Main.a_new.dip, ha.candidate.dip_cand);
feature_vector[1] = IP_sim(Main.a_new.sip, ha.candidate.sip_cand);
feature_vector[2] = IP_s_d_sim(Main.a_new.sip, ha);
feature_vector[3] = Dport_sim(Main.a_new.dport, ha);
freq_weight(Main.a_new.Atype, ha, freq_avg, weight_avg , wm);
feature_vector[4] = weight_avg;
feature_vector[5] = freq_avg;
double[] values = new double[]{feature_vector[0],feature_vector[1],feature_vector[2],feature_vector[3],feature_vector[4],feature_vector[5]};
DenseInstance newInst = new DenseInstance(1.0,values);
Instances dataUnlabeled = new Instances("TestInstances", atts, 0);
dataUnlabeled.add(newInst);
dataUnlabeled.setClassIndex(dataUnlabeled.numAttributes() - 1);
double clslable = lgb.classifyInstance(inst);
} catch (Exception ex) {
//Logger.getLogger(Module2.class.getName()).log(Level.SEVERE, null, ex);
}
return clslable;}