Security and Communication Networks

Research Article

Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine

Table 5

Classification accuracy of seven algorithms on and for real-world data sets.


	ELM	STAR-SVM	Lap-SVM	SS-ELM	D-SOS-ELM	LapTEM	LGS-OSELM
Dataset	Acc%	Acc%	Acc%	Acc%	Acc%	Acc%	Acc%
	(c)	(c, )	(c, , k)	(c, , k)	(c)	(c1, c2, k)	(c, k)

Australian	66.78	69.73	69.96	70.81	76.34	75.43	77.34
	(10⁻²)	(10⁰, 0.7)	(10⁰, 10⁻², 5)	(10², 10⁰, 10)	(10²)	(10², 0.01, 10)	(10⁻², 10)
	67.82	70.56	70.38	70.85	77.56	77.43	79.28
	(10⁻²)	(10⁰, 0.7)	(10⁰, 10⁻², 5)	(10², 10⁰, 10)	(10²)	(10², 0.01, 10)	(10⁻², 10)
EEG-Eye	73.35	94.23	73.42	76.30	93.25	94.62	97.65
	(10⁰)	(10², 0.5)	(10², 10⁻⁴, 15)	(10², 10⁰, 15)	(10²)	(10², 0.01, 10)	(10², 20)
	75.45	95.36	74.35	77.42	92.14	94.25	97.58
	(10⁰)	(10², 0.5)	(10², 10⁻⁴, 15)	(10², 10⁰, 15)	(10²)	(10², 0.01, 10)	(10², 20)
Glass	64.52	68.23	64.52	66.35	70.46	72.13	73.34
	(10⁻²)	(10⁻², 0.7)	(10⁻², 10², 5)	(10⁻², 10², 5)	(10⁻²)	(10⁻², 0.05, 15)	(10⁻², 10)
	67.52	71.66	68.14	66.63	72.45	72.34	77.42
	(10⁻²)	(10⁻², 0.7)	(10⁻², 10², 5)	(10⁻², 10², 5)	(10⁻²)	(10², 0.05, 15)	(10⁻², 10)
Heart	66.34	72.45	72.52	64.52	75.19	76.26	78.39
	(10⁰)	(10², 0.4)	(10², 10², 5)	(10⁻⁴, 10², 5)	(10⁻⁴)	(10², 0.1, 15)	(10⁰, 15)
	67.12	73.43	75.72	67.64	75.29	77.09	77.65
	(10⁰)	(10², 0.4)	(10², 10², 5)	(10⁻⁴, 10², 5)	(10⁻⁴)	(10², 0.1, 15)	(10⁰, 15)
MNIST	81.57	88.23	88.56	89.45	91.18	92.57	92.32
	(10⁰)	(10², 0.3)	(10², 10², 5)	(10⁻⁴, 10⁻², 15)	(10⁻⁴)	(10⁻⁴, 0.05, 15)	(10², 15)
	83.06	89.56	89.83	91.26	91.62	93.52	92.35
	(10⁰)	(10², 0.3)	(10², 10², 5)	(10⁻⁴, 10⁻², 15)	(10⁻⁴)	(10⁻⁴, 0.05, 15)	(10², 15)
Mushroom	88.45	90.34	87.84	90.32	92.24	89.15	91.65
	(10⁰)	(10², 0.5)	(10⁰, 10², 5)	(10⁻², 10², 10)	(10⁻²)	(10⁻², 0.1, 15)	(10⁰, 5)
	89.21	92.66	88.93	90.47	93.54	89.25	93.25
	(10⁰)	(10², 0.5)	(10⁰, 10², 5)	(10⁻², 10², 10)	(10⁻²)	(10⁻², 0.1, 15)	(10⁰, 5)
Ionosphere	82.17	86.25	84.72	84.14	90.94	90.94	92.46
	(10⁻²)	(10², 0.7)	(10⁻², 10², 15)	(10⁻², 10², 10)	(10⁻²)	(10⁻², 0.1, 15)	(10⁰, 15)
	82.43	86.37	87.82	84.43	91.46	91.42	92.86
	(10⁻²)	(10², 0.7)	(10⁻², 10², 15)	(10⁻², 10², 10)	(10⁻²)	(10⁻², 0.1, 15)	(10⁰, 15)
Pima	62.52	67.44	65.45	67.44	68.15	68.24	69.23
	(10⁻⁴)	(10⁻⁴, 0.3)	(10⁻⁴, 10⁰, 10)	(10⁻⁴, 10⁰, 15)	(10⁻⁴)	(10⁻⁴, 0.1, 5)	(10⁻⁴, 5)
	63.68	69.05	65.86	69.17	68.52	69.45	71.80
	(10⁻⁴)	(10⁻⁴, 0.3)	(10⁻⁴, 10⁰, 10)	(10⁻⁴, 10⁰, 15)	(10⁻⁴)	(10⁻⁴, 0.1, 5)	(10⁻⁴, 5)
Seeds	58.43	65.61	60.45	64.56	68.62	68.16	67.52
	(10²)	(10², 0.7)	(10², 10⁻², 10)	(10⁻⁴, 10⁰, 10)	(10⁻⁴)	(10⁻⁴, 0.01, 20)	(10², 20)
	58.44	67.26	62.16	69.30	69.12	68.62	71.35
	(10²)	(10², 0.7)	(10², 10⁻², 10)	(10⁻⁴, 10⁰, 10)	(10⁻⁴)	(10⁻⁴, 0.01, 20)	(10², 20)
Sonar	73.45	77.32	67.27	77.34	80.24	86.72	85.68
	(10⁻²)	(10⁻², 0.5)	(10⁻⁴, 10², 5)	(10⁻⁴, 10⁻², 5)	(10⁻²)	(10⁴, 0.05, 15)	(10⁴, 15)
	73.55	79.17	69.11	79.00	82.14	85.68	87.19
	(10⁻²)	(10⁻², 0.5)	(10⁻⁴, 10², 5)	(10⁻⁴, 10⁻², 5)	(10⁻²)	(10⁴, 0.05, 15)	(10⁴, 15)
USPS_20	84.44	96.52	93.04	95.26	96.72	98.15	97.75
	(10⁻²)	(10⁻², 0.6)	(10⁻⁴, 10², 5)	(10⁻², 10⁻², 5)	(10⁻²)	(10⁻², 0.1, 5)	(10⁰, 5)
	86.14	92.36	97.76	95.54	97.42	98.25	97.12
	(10⁻²)	(10⁻², 0.6)	(10⁻⁴, 10², 5)	(10⁻², 10⁻², 5)	(10⁻²)	(10⁻², 0.1, 5)	(10⁰, 5)
Wine	48.34	58.52	51.36	61.41	58.63	58.32	62.52
	(10²)	(10², 0.7)	(10², 10², 15)	(10², 10⁻², 15)	(10⁻²)	(10², 0.01, 15)	(10², 15)
	58.47	60.58	62.45	65.78	61.63	60.53	64.27
	(10²)	(10², 0.7)	(10², 10², 15)	(10², 10⁻², 15)	(10⁻²)	(10², 0.01, 15)	(10², 15)
Average	70.86	77.91	73.22	75.65	80.29	81.04	82.15
Average	72.74	79.04	76.13	77.29	81.39	81.42	83.63