Table 11.

SVM performances with parameters optimization or not based on informative genes selected by RS

Parameters optimization	Kernel	Evaluation	Leuk1	Lung1	Leuk2	SRBCT	Breast	Lung2	DLBCL	Cancers	GCM	Average
No (fixed C = 1)	linear	Fitting	97.37	95.31	100	100	100	97.06	100	100	97.92	98.63
		LOOCV	97.37	81.25	98.25	98.41	94.44	94.12	96.55	96	77.78	92.69
		Testing	94.12	84.38	93.33	95	93.33	97.01	96.67	87.84	58.7	88.93
Yes	linear	Fitting	97.37	100	100	100	100	100	100	99	100	99.6
		LOOCV	97.37	90.63	100	100	98.15	94.12	100	96	81.25	95.28
		Testing	94.12	84.38	100	95	93.33	95.52	96.67	89.19	65.22	90.38
		C	0.25	32	0.03125	0.5	0.125	8	0.25	0.25	4
No (fixed C = 1, γ = 1/m)	RBF	Fitting	97.37	87.50	100	100	100	91.18	100	88.00	45.14	89.91
		LOOCV	97.37	79.69	100	98.41	98.15	90.44	86.21	78.00	77.08	89.48
		Testing	94.12	78.13	100	95.00	93.33	97.01	93.33	85.14	52.17	87.58
Yes	RBF	Fitting	97.37	100	100	100	100	95.59	100	100	100	99.22
		LOOCV	97.37	90.63	100.00	98.00	98.15	94.12	100	98.00	82.64	95.43
		Testing	94.12	84.38	86.67	90.00	93.33	95.52	90.00	87.84	52.17	86.00
		C	8	2048	0.125	0.25	0.5	2	1	32768	32
		γ	0.0125	0.0075125	0.25	0.125	0.25	0.0625	0.25	0.00390625	0.0625

C is penalty parameters and C∈[2⁻⁵, 2¹⁵]; γ is gamma parameter in kernel function and γ∈[2⁻¹⁵, 2³]; m is features number of each SVM models