algorithm='SAMME', n_estimators=200, learning_rate=0.8) bdt.fit(Feature_train, Label_train.ravel()) Label_predict = bdt.predict(Feature_test) elif m == 'SMOTE-AdaBoost-DT': sm = SMOTE() Feature_train_o, Label_train_o = sm.fit_sample(Feature_train, Label_train.ravel()) bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5), algorithm='SAMME', n_estimators=200, learning_rate=0.8) bdt.fit(Feature_train_o, Label_train_o) Label_predict = bdt.predict(Feature_test) elif m == 'META-DES': pool_classifiers = RandomForestClassifier(n_estimators=10) pool_classifiers.fit(Feature_train, Label_train.ravel()) metades = METADES(pool_classifiers) metades.fit(Feature_train, Label_train.ravel()) Label_predict = metades.predict(Feature_test) elif m == 'MCB': 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())
y_kne_pred = kne.predict(X_test) accuracy_kne = accuracy_score(Y_test, y_kne_pred) # print('accuracy = ', accuracy_kne) micro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='micro') # print('micro_f1 =', micro_f1_kne) macro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='macro') # print('macro_f1 =', macro_f1_kne) test_time_kne_end = time.time() #---------------------- Test META DES Pharse -------------------------- test_time_mdes_start = time.time() y_mdes_pred = meta.predict(X_test) accuracy_mdes = accuracy_score(Y_test, y_mdes_pred) # print('accuracy = ', accuracy_mdes) micro_f1_mdes = f1_score(Y_test - 1, y_mdes_pred - 1, average='micro') # print('micro_f1 =', micro_f1_mdes) # print('support_macro:',precision_recall_fscore_support(Y_test, y_mdes_pred, average='macro')) macro_f1_mdes = f1_score(Y_test - 1, y_mdes_pred - 1, average='macro') # print('macro_f1 =', macro_f1_mdes) # print('micro:', precision_recall_fscore_support(Y_test, y_mdes_pred, average='micro')) test_time_mdes_end = time.time() #---------------------- Test KNU Pharse -------------------------- test_time_knu_start = time.time() y_knu_pred = knu.predict(X_test)