An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection

Table 2

Experimental results for EABC-SVM and ABC-SVM method.

Dataset	Method	Opt Acc	Ave Acc	Acc SD	value for -test	Selected dimensions
WDBC	ABC-SVM	96.13	94.57	1.05	<0.001	13.70 ± 1.73
	EABC-SVM	98.24	97.25	0.71		12.15 ± 1.42
Ionosphere	ABC-SVM	95.44	93.93	1.24	<0.001	14.95 ± 1.80
	EABC-SVM	96.30	95.63	0.38		12.55 ± 0.86
Musk1	ABC-SVM	92.65	87.20	2.98	<0.001	83.40 ± 3.17
	EABC-SVM	95.59	93.04	1.70		76.30 ± 3.10
Sonar	ABC-SVM	89.90	87.86	1.20	<0.001	29.00 ± 2.73
	EABC-SVM	93.27	91.54	0.78		26.80 ± 1.69
Vehicle	ABC-SVM	80.26	77.06	2.23	<0.001	12.00 ± 1.52
	EABC-SVM	85.58	85.30	0.50		10.50 ± 0.81
Wine	ABC-SVM	98.88	95.86	1.73	<0.001	6.20 ± 1.08
	EABC-SVM	100	99.07	1.04		5.95 ± 0.59

Confidence level: 95%.