Table 4. Results on average classification accuracy of RCSA vs exiting algorithms.

Dataset	WBCO	Glass	Wine	Zoo	Vehicle	Protein	Segment	Wdbc	Soybean	Lung cancer	Sonar
DFS	0.9337	0.7157	0.9514	0.9687	0.7336	0.788	0.9674	0.9342	1	0.9269	0.9265
MBOICO	0.934	0.7186	0.9563	0.9775	0.727	0.8173	0.9665	0.9418	1	0.9846	0.9668
MBOLF	0.8741	0.7104	0.95	0.965	0.7302	0.8348	0.9693	0.9432	1	0.9846	0.9674
FS-BGSK	0.939	0.786	0.9563	0.9775	0.7303	0.8195	0.966	0.9381	1	0.95	0.951
BGSA	0.9387	0.7535	0.9537	0.961	0.7337	0.7826	0.9751	0.9342	1	0.9538	0.9324
FS-pBGSK	0.941	0.7462	0.9605	0.9787	0.7363	0.8304	0.9706	0.9394	1	0.98	0.946
HGSA	0.9306	0.6973	0.9114	0.9725	0.7225	0.7923	0.9301	0.9399	1	0.95	0.9502
DGUFS	0.9282	0.6773	0.9394	0.9662	0.7312	0.8141	0.9662	0.9373	1	0.9961	0.9583
WOASAT	0.9247	0.6359	0.9239	0.965	0.7219	0.8087	0.9507	0.9298	1	0.9692	0.9181
BSSA	0.9311	0.6767	0.938	0.97	0.697	0.8174	0.969	0.9281	1	0.9385	0.9398
BRCSA	0.942	0.819	0.96	0.985	0.731	0.843	0.966	0.951	1	0.9962	0.97
Without FS	0.96	0.671	0.732	0.932	0.69	0.691	0.96	0.916	0.984	0.461	0.816