Table 5.

Comparison of ELM/RELM with the traditional classifiers on the CK dataset.

Classifiers	AlexNet			DenseNet			GoogleNet			Inceptionv3			ResNet101
	Accuracy	Precision	F1-score	Accuracy	Precision	F1-score	Accuracy	Precision	F1-score	Accuracy	Precision	F1-score	Accuracy	Precision	F1-score
RELM	0.82	0.80	0.83	0.83	0.85	0.82	0.78	0.75	0.78	0.78	0.69	0.74	0.82	0.84	0.82
ELM	0.83	0.79	0.82	0.73	0.75	0.74	0.72	0.76	0.70	0.78	0.79	0.79	0.56	0.46	0.52
SVM	0.80	0.80	0.81	0.77	0.75	0.77	0.80	0.80	0.80	0.77	0.75	0.77	0.82	0.82	0.80
Naïve Bayes	0.23	0.25	0.25	0.20	0.28	0.25	0.29	0.28	0.28	0.24	0.25	0.24	0.43	0.45	0.45
Random forest	0.45	0.49	0.46	0.76	0.79	0.75	0.53	0.48	0.52	0.52	0.53	0.52	0.70	0.77	0.75
K-nearest neighbors	0.57	0.59	0.56	0.60	0.68	0.69	0.60	0.60	0.61	0.53	0.54	0.59	0.79	0.79	0.77
Logistic regression	0.29	0.31	0.29	0.53	0.52	0.52	0.55	0.59	0.56	0.58	0.60	0.59	0.59	0.60	0.61
Random tree	0.26	0.28	0.25	0.53	0.55	0.50	0.75	0.75	0.73	0.57	0.57	0.57	0.55	0.53	0.57
Simple logistic	0.48	0.47	0.48	0.73	0.74	0.70	0.73	0.75	0.75	0.75	0.78	0.75	0.74	0.74	0.74
Decision table	0.23	0.20	0.23	0.48	0.45	0.46	0.74	0.70	0.74	0.43	0.45	0.40	0.42	0.42	0.42
Multiclass classifier	0.57	0.62	0.60	0.65	0.67	0.67	0.68	0.74	0.70	0.74	0.74	0.74	0.70	0.74	0.70
Multilayer perceptron	0.43	0.43	0.41	0.65	0.70	0.69	0.78	0.68	0.67	0.68	0.65	0.66	0.65	0.68	0.68