Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

Table 6

The comparisons of training and testing on the classification cases.

Datasets	Algorithms	Training accuracy ()	Training time (s)	Testing accuracy ()	Testing deviation ()
Delta Ailerons	SVM	95.41	219.54	95.42	0
	ELM	95.78	43.88	95.81	0.22
	ML-ELM	97.32	317.83	97.34	0.19
	AE-S-ELMs	97.75	279.32	97.71	0.27
	DBN	98.34	4432	97.35	0.05
	ErrCor	96.42	234.27	96.48	0.12
	PC-ELM	96.56	262.69	96.61	0.08
	OCI-ELM	96.73	249.89	96.69	0.07
	DOC-IELM-AEs	98.89	350.17	98.90	0.05
Waveform II	SVM	95.45	247.11	95.44	0.01
	ELM	95.71	107.14	95.73	0.16
	ML-ELM	97.83	304.28	97.86	0.11
	AE-S-ELMs	98.09	365.83	98.11	0.15
	DBN	97.59	4101	97.52	0.04
	ErrCor	96.54	261.44	96.58	0.06
	PC-ELM	96.46	293.85	96.47	0.05
	OCI-ELM	97.33	202.08	97.36	0.08
	DOC-IELM-AEs	98.86	332.58	98.85	0.05
MNIST	SVM	95.01	2108	95.03	0.03
	ELM	95.33	1114	95.32	0.31
	ML-ELM	96.78	3793	96.82	0.17
	AE-S-ELMs	96.93	3772	96.94	0.21
	DBN	96.67	14117	96.72	0.03
	ErrCor	96.17	4065	96.26	0.13
	PC-ELM	96.21	3049	96.24	0.09
	OCI-ELM	96.38	3092	96.39	0.09
	DOC-IELM-AEs	97.89	3985	97.94	0.06
OCR Letters	SVM	88.27	770.24	88.41	0.03
	ELM	89.02	265.01	89.03	0.22
	ML-ELM	89.19	923.23	89.17	0.14
	AE-S-ELMs	89.44	960.06	89.46	0.19
	DBN	88.98	15212	89.02	0.04
	ErrCor	88.81	1014	88.82	0.10
	PC-ELM	89.17	979.62	89.16	0.05
	OCI-ELM	89.36	992.08	89.39	0.08
	DOC-IELM-AEs	90.85	1175	90.86	0.06
NORB	SVM	91.13	723.81	91.14	0.04
	ELM	91.42	66.07	91.45	0.14
	ML-ELM	92.67	1107	92.70	0.17
	AE-S-ELMs	92.81	698.21	92.82	0.12
	DBN	92.58	44502	92.59	0.03
	ErrCor	91.77	1287	91.81	0.08
	PC-ELM	91.80	896.63	91.83	0.06
	OCI-ELM	92.61	839.48	92.65	0.05
	DOC-IELM-AEs	94.53	1480	94.56	0.03