Table 3.

Performance comparison of different methods on Yeast dataset.

Author	Model	ACC (%)	PE (%)	SN (%)	MCC (%)
Guos’ work [34]	ACC	89.33 ± 2.67	88.87 ± 6.16	89.93 ± 3.68	N/A
	AC	87.36 ± 1.38	87.82 ± 4.33	87.30 ± 4.68	N/A
You et al.’s work [17]	PCA-EELM	87.00 ± 0.29	87.59 ± 0.32	86.15 ± 0.43	77.36 ± 0.44
Yang et al.’s work [31]	Cod1	75.08 ± 1.13	74.75 ± 1.23	75.81 ± 1.20	N/A
	Cod2	80.04 ± 1.06	82.17 ± 1.35	76.77 ± 0.69	N/A
	Cod3	80.41 ± 0.47	81.86 ± 0.99	78.14 ± 0.90	N/A
	Cod4	86.15 ± 1.17	90.24 ± 1.34	81.03 ± 1.74	N/A
Zhou et al.’s work [32]	SVM + LD	88.56 ± 0.33	89.50 ± 0.60	87.37 ± 0.22	77.15 ± 0.68
Wang et al.’s work [36]	PCVM + ZM	94.48 ± 1.2	93.92 ± 2.4	95.13 ± 2.0	89.58 ± 2.2
Our method	SVM + PSSM	86.99 ± 0.43	88.05 ± 0.88	85.62 ± 1.23	77.36 ± 0.64
	RF + PSSM	92.12 ± 0.54	94.20 ± 0.78	89.76 ± 0.96	85.46 ± 0.92

ACC: Auto Cross Covariance; AC: Auto Covariance; PCA-EELM: Principal component analysis-ensemble extreme learning machine; LD: Local description; PCVM + ZM: Probabilistic Classification Vector Machines+ Zernike Moments.