Table 6.

The performance of different classification algorithms on 2-class-dataset according to gose0. Significant values are in bold.

Algorithm	Acc (%)	Acc rank	Prec (%)	Rec (%)	AUC
NB	81.67 ± 1.29	7	52.61% ± 3.42	51.71% ± 7.88	0.820 ± 0.033
RF	84.45 ± 1.29	1	76.72% ± 12.75	27.89% ± 7.10	0.827 ± 0.046
KNN(k = 5)	80.64 ± 2.40	10	50.11% ± 13.99	24.14% ± 7.53	0.659 ± 0.048
KNN(k = 6)	81.07 ± 2.43	9	51.48 ± 13.05	24.13 ± 9.87	0.679 ± 0.056
DT	82.46 ± 1.15	6	59.98 ± 6.95	30.14 ± 8.62	0.703 ± 0.038
RI	83.24 ± 2.94	4	61.84 ± 12.20	39.82 ± 8.62	0.797 ± 0.067
DL	81.13 ± 2.77	8	52.81 ± 8.79	55.18 ± 12.29	0.845 ± 0.029
GBT	82.82 ± 1.72	5	55.62 ± 3.88	51.72 ± 13.27	0.827 ± 0.046
LR	84.03 ± 1.76	2	64.80 ± 8.94	40.08 ± 8.01	0.842 ± 0.043
GLM	83.91 ± 2.08	3	63.39 ± 9.43	41.08 ± 6.06	0.841 ± 0.039
Avg Acc	82.52