Selecting the Optimal Combination Model of FSSVM for the Imbalance Datasets

Table 3

FSSVMs compare with SSVM and the normal SVM on five datasets for G-means (%) and time (s) (exclusive the time of the preprocessing normal SVM). All of the SSVMs are based on the Newton-Armijo algorithm and the two kinds of fuzzy membership functions and two kinds of smooth functions are used in the FSSVMs.

	SSVM			FSSVM
Data				Linear decaying			Exponential decaying
	N-SVM	S𝑠-SVN	S𝑝4-SVM	FS𝑠-SVM	FS𝑝4-SVM	FSVM	FS𝑠-SVM	FS𝑝4-SVM	FSVM
Abanole	19.39	17.80	19.84	71.86	72.96	29.92	70.45	70.16	31.24
	14.32	0.89	0.98	0.56	0.28	1.56	0.28	0.24	1.26
Yeast	67.68	58.46	62.06	80.40	85.54	72.06	82.54	84.20	71.46
	56.23	8.06	4.56	8.24	5.20	14.48	3.88	3.26	12.48
Satimage	81.05	81.86	82.54	90.32	91.46	90.28	93.72	94.40	91.28
	25.91	12.24	16.45	15.56	17.28	28.24	11.86	11.71	26.42
Pima	68.86	69.24	70.56	71.88	72.28	70.64	72.43	73.68	71.04
	3.54	1.12	1.36	0.86	0.46	1.88	0.46	0.24	1.68
Haberman	42.46	52.20	50.56	61.24	62.60	60.26	64.96	66.48	61.48
	1.20	0.12	0.36	0.16	0.11	0.56	0.16	0.08	0.24