Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

Table 4

Classification accuracy of each ABC algorithm on the tested datasets by using NN 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 (%)	93.92 ± 0.34	88.32 ± 0.90	92.19 ± 0.46	97.43 ± 0.29	85.87 ± 0	99.44 ± 0.12
	Average number of features	24.3	34	9.3	20.6	9.5	25.2
EABC	Accuracy (%)	93.66 ± 0.31	87.40 ± 0.54	91.31 ± 0.33	95.57 ± 0.31	84.46 ± 0.50	99.44 ± 0.12
	Average number of features	26.8	35.5	10.3	20.8	12.1	26.3
GABC	Accuracy (%)	93.79 ± 0.37	88.89 ± 1.07	91.57 ± 0.50	96.58 ± 0.58	85.87 ± 0	99.42 ± 0.13
	Average number of features	24.5	33.9	11.7	21.2	9.3	29.2
GDABC	Accuracy (%)	93.66 ± 0.29	88.51 ± 0.94	92.39 ± 0.42	96.04 ± 0.51	85.87 ± 0	99.41 ± 0.10
	Average number of features	24.4	32.7	9.8	22.5	10	28.2
MABC	Accuracy (%)	94.06 ± 0.35	88.70 ± 1.09	91.54 ± 0.53	97.98 ± 0.32	85.87 ± 0	99.41 ± 0.10
	Average number of features	25	29.8	8.4	20.2	7.1	27.8
OABC	Accuracy (%)	93.48 ± 0.34	92.74 ± 0.86	91.40 ± 0.35	97.43 ± 0.29	85.27 ± 0.28	99.41 ± 0.10
	Average number of features	25.1	33.1	11.6	7.8	7.8	25.8