Table 3 Performance of BNs in different datasets.

Algorithms	Dataset	Accuracy	Sensitivity	Specificity	PPV	NPV	AUC	MCC	G-mean
Tabu	Original Data	0.854	0.069	0.989	0.511	0.861	0.695	0.146	0.260
	SMOTE	0.646	0.684	0.607	0.635	0.658	0.692	0.292	0.644
	BL-SMOTE	0.644	0.694	0.604	0.633	0.656	0.694	0.288	0.642
	SMOTE-ENN	0.863	0.714	0.931	0.825	0.877	0.913	0.673	0.815
Hill-climbing	Original Data	0.854	0.067	0.989	0.511	0.861	0.705	0.145	0.258
	SMOTE	0.646	0.687	0.605	0.635	0.659	0.694	0.292	0.645
	BL-SMOTE	0.645	0.685	0.605	0.634	0.658	0.692	0.291	0.644
	SMOTE-ENN	0.860	0.703	0.932	0.826	0.873	0.912	0.666	0.810
MMHC	Original Data	0.854	0.000	1.000	-	0.854	0.674	-	0.000
	SMOTE	0.619	0.733	0.504	0.597	0.654	0.668	0.244	0.608
	BL-SMOTE	0.623	0.739	0.507	0.600	0.660	0.670	0.253	0.612
	SMOTE-ENN	0.857	0.668	0.943	0.842	0.862	0.900	0.656	0.794

Quick links

Search