Table 8.

Performance of different classification algorithms on the data, where X* denotes the optimized classifier X.

Classifier	Accuracy	TPR	TNR	AUC	Precision	F-Score	G-Mean	Mean Rank
DT	0.5876	0.6309	0.5360	0.5834	0.6184	0.6246	0.5815	8.97
LDA	0.6861	0.7584	0.6000	0.6792	0.6933	0.7244	0.6746	5.79
LR	0.6861	0.7383	0.6240	0.6811	0.7006	0.7190	0.6787	5.57
NB	0.6642	0.7785	0.5280	0.6533	0.6629	0.7160	0.6411	7.71
SVM	0.6898	0.8188	0.5360	0.6774	0.6778	0.7416	0.6625	5.71
kNN	0.6934	0.7517	0.6240	0.6878	0.7044	0.7273	0.6849	4.21
Ensemble	0.7044	0.8188	0.5680	0.6934	0.6932	0.7508	0.6820	3.93
SVM*	0.7226	0.7919	0.6400	0.7160	0.7239	0.7564	0.7119	2.14
kNN*	0.7409	0.8322	0.6320	0.7321	0.7294	0.7774	0.7252	1.14