Effect Improved for High-Dimensional and Unbalanced Data Anomaly Detection Model Based on KNN-SMOTE-LSTM

Table 5

Comparison of detection results of basic classifier.

Basic classifier	Error rate	Accuracy	Precision	Recall	F-score	AUC
Gaussian naive Bayes (GaussianNB)	0.0176	0.9824	0.1026	0.8537	0.1832	0.9163
Logistic regression	0.0008	0.9992	0.8492	0.6740	0.7516	0.8296
AdaBoost classifier	0.0008	0.9992	0.8113	0.7143	0.7597	0.8611
k-nearest neighbor classifier (kNN)	0.0007	0.9993	0.9228	0.6726	0.7781	0.8373
BP neural network	0.0007	0.9993	0.8894	0.7189	0.7952	0.8591
Gradient boosted decision tree (GBDT)	0.0006	0.9994	0.9175	0.7154	0.8040	0.8604
Support vector machine (SVM)	0.0007	0.9993	0.8170	0.8022	0.8095	0.9012
Random forest (RF)	0.0005	0.9995	0.9313	0.7850	0.8519	0.8902
LSTM	0.0005	0.9995	0.8723	0.8255	0.8483	0.9132