Binary Bitwise Artificial Bee Colony as Feature Selection Optimization Approach within Taguchi’s T-Method

Table 6

The prediction accuracy (MAE) compilation across all proposed enhanced methods and existing Taguchi’s T-Method on validation datasets for the small sample.

Dataset	# sample training	# sample testing	# Features	Measure	T-Method [28]	T Method-PBPSO [26]	T Method-BitABC	T-Method + OA [28]	% MAE improvement of best result vs. T-Method
Chiller	10	4	44	MAE	4.7925	5.3121	5.6858	4.3354	9.54%
				SD	6.2396	3.6373	4.3237	4.0135
				# optimum features	44	12	14	16
JD power	14	6	44	MAE	0.7866	0.9127	0.7153	0.7299	9.07%
				SD	0.7235	0.4276	0.5361	0.7431
				# optimum features	44	24	27	23