Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

Table 8

Classification accuracies of gene expression data obtained via different classification methods.

Datasets	Methods								HICATS
	Non-SVM			MC-SVM					SVM
	NN [9]	NN	PNN	OVR	OVO	DAG	WW	CS	OVR
9_Tumors	78.33	19.38	34.00	65.10	58.57	60.24	62.24	65.33	83.33
11_Tumors	93.10	54.14	77.21	94.68	90.36	90.36	94.68	95.30	97.70
Brain_Tumor 1	94.44	84.72	79.61	91.67	90.56	90.56	90.56	90.56	94.44
Brain_Tumor 2	94.00	60.33	62.83	77.00	77.83	77.83	73.33	72.83	94
Leukemia 1	100	76.61	85.00	97.50	91.32	96.07	97.50	97.50	100
Leukemia 2	100	91.03	83.21	97.32	95.89	95.89	95.89	95.89	100
Lung_Cancer	96.55	87.80	85.66	96.05	95.59	95.59	95.55	96.55	97.04
SRBCT	100	91.03	79.50	100	100	100	100	100	100
Prostate_Tumor	92.16	79.18	79.18	92.00	92.00	92.00	92.00	92.00	98.04
DLBCL	100	89.64	80.89	97.50	97.50	97.50	97.50	97.50	100

() Non-SVM: traditional classification method. () MC-SVM: multiclass support vector machines. () NN: -nearest neighbors. () NN: backpropagation neural networks. () PNN: probabilistic neural networks. () OVR: one-versus-the-rest. () OVO: one-versus-one. () DAG: DAGSVM. () WW: method by Weston and Watkins. () CS: method by Crammer and Singer. (11) HICATS: improved binary imperialist competition algorithm.