Table 13.

Experimental results on Hungarian dataset with the best feature subset

Mean ± SD	RF	LR	SVM	ELM	KNN	Proposed ensemble
E (%)	80.43 ± 5.37	82.07 ± 7.12	78.91 ± 5.61	80.40 ± 6.86	75.43 ± 8.64	89.47 ± 3.06
Precision (%)	75.52 ± 5.96	77.93 ± 8.48	74.48 ± 6.54	75.86 ± 7.09	66.55 ± 14.99	89.31 ± 4.44
Recall (%)	60.19 ± 16.84	62.08 ± 15.89	53.38 ± 17.93	59.42 ± 19.49	61.36 ± 19.71	82.39 ± 5.73
G-mean	71.04 ± 8.34	73.72 ± 10.15	67.55 ± 9.21	70.97 ± 10.16	59.97 ± 24.07	82.95 ± 4.63
MC (%)	87.93  ± 34.95	82.76 ± 37.63	100.00 ± 36.09	90.34 ± 44.33	94.14 ± 30.89	38.28 ± 12.10
Specificity (%)	86.34 ± 9.83	88.99 ± 7.79	89.10 ± 11.61	88.13 ± 9.92	70.92 ± 25.22	92.02 ± 5.76
AUC (%)	74.07 ± 9.16	76.31 ± 10.87	71.96 ± 10.98	74.59 ± 9.55	69.07 ± 9.98	88.38 ± 5.36

The average $+-$ sd on 10-folds CV. The best result is bolded