Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

Table 3

Classification accuracy of each ABC algorithm on the tested datasets by using SVM as an induction algorithm.

		SN	SR	IS	DE	HC	AN
Without feature selection	Accuracy (%)	91.20 ± 0.24	86.15 ± 0.87	87.09 ± 0.48	94.64 ± 0.39	79.10 ± 1.21	99.13 ± 0.11
	Average number of features	35	60	34	34	22	38
CABC	Accuracy (%)	85.35 ± 0.720	70.67 ± 0.93	94.30 ± 1.45	97.26 ± 5.22	85.86 ± 1.37	97.77 ± 0.15
	Average number of features	12.3	17.6	9.2	13.6	4.5	8.4
EABC	Accuracy (%)	85.94 ± 0.52	73.07 ± 0.01	94.01 ± 0.36	97.26 ± 0.08	85.86 ± 0.5	97.77 ± 0.21
	Average number of features	7.8	10.2	8.9	13.4	4.1	7.9
GABC	Accuracy (%)	85.51 ± 0.31	72.11 ± 1.23	94.30 ± 0.55	96.45 ± 0.88	86.14 ± 0.1	97.77 ± 0.23
	Average number of features	9.1	18.0	6.7	8.3	5.6	13.7
GDABC	Accuracy (%)	85.36 ± 0.59	73.55 ± 1.03	94.30 ± 0.18	96.72 ± 0.17	85.86 ± 0.93	97.77 ± 0.08
	Average number of features	9.4	22.2	11.8	8.9	4.2	15.8
MABC	Accuracy (%)	86.67 ± 0.62	75.96 ± 1.22	95.16 ± 0.46	98.63 ± 0.68	86.41 ± 0.27	97.77 ± 0.01
	Average number of features	12.3	8.5	7.4	8.5	3.9	10.0
OABC	Accuracy (%)	84.77 ± 0.65	73.08 ± 0.61	94.30 ± 0.47	96.99 ± 0.30	85.87 ± 1.35	97.77 ± 0.35
	Average number of features	5.8	10.3	5.7	4.9	5.9	15.7