Table 9.

Experimental results on Cleveland dataset with the best feature subset

Mean ± SD	RF	LR	SVM	ELM	KNN	Proposed ensemble
E (%)	86.78 ± 6.15	86.53 ± 6.75	86.50 ± 5.89	84.19 ± 7.59	79.44 ± 9.05	93.83 ± 4.93${}^{\ast }$
Precision (%)	82.67 ± 7.28	83.00 ± 7.45	82.00 ± 6.25	79.00 ± 8.32	72.00 ± 11.88	88.67 ± 5.49
Recall (%)	80.26 ± 14.28	78.02 ± 16.41	81.20 ± 15.12	77.86 ± 19.94	73.70 ± 14.34	89.68 ± 8.78
G-mean	82.24 ± 8.84	82.24 ± 9.12	81.51 ± 8.03	78.77 ± 11.80	72.01 ± 11.84	90.77 ± 6.71
MC (%)	54.67  ± 33.45	59.67 ± 38.12	54.00 ± 35.38	63.67 ± 42.95	78.67 ± 39.79	22.00 ± 15.61
Specificity (%)	84.63 ± 7.49	87.49 ± 6.38	82.47 ± 6.54	80.42 ± 7.43	71.26 ± 14.11	89.31 ± 5.13
AUC (%)	81.53 ± 8.75	81.99 ± 9.38	80.91 ± 8.14	79.99 ± 11.05	70.53 ± 12.65	89.54 ± 5.54

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