Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

Table 3

The comparisons of training and testing on the classification cases.

Datasets	Approaches	Testing accuracy			⁢Hidden nodes & average time
		Nodes (fixed)	Mean ()	Std.	# nodes	Time (s)
Delta Ailerons (0.035)	CI-ELM	250	83.29	0.0036	369.32	1.3505
	PC-ELM	250	90.02	0.0016	35.19	0.6829
	LOO-IELM	250	91.17	0.0027	41.12	0.7761
	SB-ELM	250	91.66	0.0071	≈220	0.0556
	II-RELM	250	91.18	0.0042	51.16	0.7425
	EIR-ELM	250	92.03	0.0019	34.29	1.1304
	OCI-ELM	250	92.84	0.0012	31.73	0.7021
Waveform II (0.04)	CI-ELM	100	84.47	0.0182	200.11	3.0977
	PC-ELM	100	89.81	0.0104	47.63	3.0954
	LOO-IELM	250	88.93	0.0097	46.44	3.3437
	SB-ELM	250	80.69	0.0181	—	—
	II-RELM	250	90.64	0.0112	44.33	3.6603
	EIR-ELM	250	91.15	0.0096	38.91	3.2267
	OCI-ELM	100	93.11	0.0083	29.54	3.0864
Abalone (0.05)	CI-ELM	150	82.72	0.0022	150.37	0.4930
	PC-ELM	150	93.57	0.0016	24.62	0.6177
	LOO-IELM	250	90.51	0.0033	38.43	0.7102
	SB-ELM	250	86.03	0.0107	≈180	0.0413
	II-RELM	250	92.10	0.0034	35.74	0.6924
	EIR-ELM	250	93.91	0.0018	24.16	0.8533
	OCI-ELM	150	94.24	0.0015	21.41	0.6802
Breast Cancer (0.07)	CI-ELM	200	90.06	0.0145	88.30	0.0804
	PC-ELM	200	93.23	0.0082	34.79	0.0992
	LOO-IELM	250	93.07	0.0104	40.82	0.1102
	SB-ELM	250	94.41	0.0075	≈150	0.0275
	II-RELM	250	92.58	0.0095	55.27	0.1032
	EIR-ELM	250	94.76	0.0078	31.18	0.1174
	OCI-ELM	200	94.73	0.0067	34.58	0.1061
Energy Efficiency (0.055)	CI-ELM	150	91.78	0.0013	61.09	0.2966
	PC-ELM	150	96.53	0.0008	41.08	0.3617
	LOO-IELM	250	95.18	0.0024	27.94	0.3517
	SB-ELM	250	92.16	0.0033	≈150	0.0658
	II-RELM	250	95.29	0.0011	46.83	0.3826
	EIR-ELM	250	96.67	0.0012	22.42	0.4011
	OCI-ELM	150	97.25	0.0008	18.49	0.3979