An Interior Point Method for 𝐿_1/2-SVM and Application to Feature Selection in Classification

Table 1

Results comparison on 10 artificial data sets with various training sample sizes.

𝑛	𝐿2-SVM		𝐿1-SVM		𝐿0-SVM		𝐿1/2-SVM
	Card	Acc	Card	Acc	Card	Acc	Card	Acc
10	29.97	84.65	6.93	88.55	1.43	77.65	2.25	88.05
20	30.00	92.21	8.83	96.79	1.87	93.17	2.35	95.50
30	30.00	94.27	8.47	98.23	2.03	94.67	2.05	97.41
40	29.97	95.99	9.00	98.75	2.07	95.39	2.10	97.75
50	30.00	96.61	9.50	98.91	2.13	96.57	2.45	97.68
60	29.97	97.25	10.00	98.94	2.20	97.61	2.45	97.98
70	30.00	97.41	11.00	98.98	2.20	97.56	2.40	98.23
80	30.00	97.68	10.03	99.09	2.23	97.61	2.60	98.50
90	29.97	97.89	9.20	99.21	2.30	97.41	2.90	98.30
100	30.00	98.13	10.27	99.09	2.50	97.93	2.55	98.36
On average	29.99	95.21	9.32	97.65	2.10	94.56	2.41	96.78

“𝑛” is the number of training samples, “Card” represents the number of selected features, and “Acc” is the classification accuracy.