pool_classifiers = RandomForestClassifier(n_estimators=10)
                pool_classifiers.fit(Feature_train, Label_train.ravel())
                mcb = MCB(pool_classifiers)
                mcb.fit(Feature_train, Label_train.ravel())
                Label_predict = mcb.predict(Feature_test)
            elif m == 'DES-MI':
                pool_classifiers = RandomForestClassifier(n_estimators=10)
                pool_classifiers.fit(Feature_train, Label_train.ravel())
                dmi = DESMI(pool_classifiers)
                dmi.fit(Feature_train, Label_train.ravel())
                Label_predict = dmi.predict(Feature_test)
            elif m == 'One_vs_Rest-SMOTE-XGBoost':
                sm = SMOTE()
                Feature_train_o, Label_train_o = sm.fit_sample(Feature_train, Label_train.ravel())
                clf = OneVsRestClassifier(xgboost.XGBClassifier())
                clf.fit(Feature_train_o, Label_train_o)
                Label_predict = clf.predict(Feature_test)
            elif m == 'One_vs_Rest-XGBoost':
                clf = OneVsRestClassifier(xgboost.XGBClassifier())
                clf.fit(Feature_train, Label_train.ravel())
                Label_predict = clf.predict(Feature_test)

            ml_record.measure(i, Label_test, Label_predict, 'weighted')
            i += 1

        file_wirte = "Result_One_vs_All.txt"
        ml_record.output(file_wirte, m, Dir)



                #print("Data Set Folder: ", file, ", SMOTE folder id: ", str(k))
                Feature_train_smote = np.concatenate(
                    (SMOTE_feature_train_list[k][0],
                     SMOTE_feature_valid_list[k][0]))
                Label_train_smote = np.concatenate(
                    (SMOTE_label_train_list[k][0],
                     SMOTE_label_valid_list[k][0]))
                pool_classifiers[k].fit(Feature_train_smote,
                                        Label_train_smote.ravel())

            if m == 'META-DES-XGBoost':
                metades = METADES(pool_classifiers)
                metades.fit(Feature_train, Label_train.ravel())
                Label_predict = metades.predict(Feature_test)
            elif m == 'MCB-XGBoost':
                mcb = MCB(pool_classifiers)
                mcb.fit(Feature_train, Label_train.ravel())
                Label_predict = mcb.predict(Feature_test)
            elif m == 'DES-MI-XGBoost':
                dmi = DESMI(pool_classifiers)
                dmi.fit(Feature_train, Label_train.ravel())
                Label_predict = dmi.predict(Feature_test)

        print(confusion_matrix(Label_test, Label_predict))

        ml_record.measure(i, Label_test, Label_predict, 'weighted')
        i += 1

    file_wirte = "Result_Esemble_Tang.txt"
    ml_record.output(file_wirte, m, 'Tang')