Support vector machine with hypergraph-based pairwise constraints

Hou, Qiuling; Lv, Meng; Zhen, Ling; Jing, Ling

doi:10.1186/s40064-016-3315-x

SpringerPlus

Table 3 Test accuracy on UCI datasets for nonlinear classifiers

From: Support vector machine with hypergraph-based pairwise constraints

Datasets	SVM			LSSVM			SRSVM			MPCSVM			HPCSVM
Datasets	Accuracy	Time (s)	p value	Accuracy	Time (s)	p value	Accuracy	Time (s)	p value	Accuracy	Time (s)	p value	Accuracy	Time (s)
Hepatitis (155 × 19)	82.01 ± 9.43	0.10	0.7550	83.34 ± 8.36	0.08	0.9095	83.78 ± 8.12	0.12	0.9643	84.01 ± 7.98	0.16	0.9971	84.03 ± 8.23	0.14
Heartstatlog (270 × 13)	82.96 ± 2.46	0.27	0.5264	83.70 ± 6.13	0.21	0.7863	84.70 ± 5.24	0.33	0.9654	84.44 ± 5.32	0.41	0.9218	84.81 ± 5.02	0.36
Teaching (151 × 5)	82.14 ± 3.19	0.10	0.7812	82.14 ± 3.19	0.07	0.7812	83.24 ± 3.45	0.11	0.8574	82.83 ± 3.59	0.13		82.83 ± 3.59	0.12
Haberman (306 × 3)	73.53 ± 0.48	0.35	0.1056	74.51 ± 3.03	0.26	0.5231	75.32 ± 2.65	0.46	0.8032	75.48 ± 2.98	0.50	0.8693	75.81 ± 2.45	0.44
Breast (683 × 9)	95.60 ± 4.54	2.20	0.4155	97.22 ± 1.50	1.34	0.6948	97.62 ± 1.24	3.26	0.9544	97.51 ± 1.36	3.51	0.8913	97.66 ± 1.56	3.01
BUPA (345 × 6)	71.01 ± 3.30	0.46	0.0602	73.33 ± 2.67	0.42	0.2547	74.43 ± 2.49	0.48	0.5642	75.36 ± 2.75	0.68	0.8832	75.65 ± 2.66	0.65
Diabetes (768 × 8)	72.39 ± 1.86	2.57	0.0384	77.48 ± 4.65	1.68	0.7826	76.48 ± 4.26	2.86	0.5145	78.01 ± 5.01	3.89	0.9104	78.40 ± 4.48	3.44
Seeds (210 × 7)	95.95 ± 3.10	0.17	0.1614	96.19 ± 2.43	0.13	0.7599	96.24 ± 2.10	0.20	0.5462	96.19 ± 1.17	0.25	0.6666	96.01 ± 1.78	0.23
Sonar (208 × 60)	88.47 ± 6.96	0.17	1	88.47 ± 6.96	0.13	1	89.59 ± 6.56	0.21	0.7459	88.47 ± 6.96	0.28		88.47 ± 6.96	0.23
Parkinsons (195 × 22)	87.21 ± 2.68	0.14	0.0016	93.87 ± 2.01	0.12	0.2447	94.28 ± 2.26	0.18	0.5568	95.95 ± 2.54	0.24	0.9888	95.92 ± 2.58	0.19
Spect (267 × 44)	79.79 ± 4.06	0.27	0.2639	82.40 ± 4.94	0.22	0.7521	83.40 ± 4.72	0.35	0.9325	83.89 ± 5.36	0.45	0.9181	83.51 ± 4.67	0.42
Ionosphere (351 × 33)	94.31 ± 2.54	0.50	0.2920	95.72 ± 1.29	0.36	0.7860	95.45 ± 2.08	0.58	0.6734	96.29 ± 1.72	0.78	0.8215	96.02 ± 1.65	0.67
Heartcancer (303 × 14)	96.38 ± 2.62	0.35	0.0247	95.05 ± 3.12	0.27	0.0131	98.05 ± 2.96	0.46	0.5672	100 ± 0.00	0.54	NaN	100 ± 0.00	0.46
Heart_disease (294 × 13)	75.85 ± 2.71	0.31	0.0178	79.31 ± 4.76	0.24	0.7579	82.31 ± 4.26	0.38	0.8973	83.00 ± 3.53	0.46	0.9085	83.33 ± 4.24	0.40
Fertility (100 × 9)	87.12 ± 4.45	0.05	0.2239	88.18 ± 4.61	0.03	0.5031	88.08 ± 1.94	0.06	0.4589	90.08 ± 2.87	0.07		90.08 ± 2.87	0.06
Ech-diogram (131 × 10)	87.68 ± 5.25	0.07	0.6181	86.16 ± 5.95	0.06	0.2633	89.24 ± 5.64	0.09	0.8575	89.96 ± 6.41	0.10	0.9951	89.99 ± 7.20	0.09
Balancescale (576 × 4)	97.92 ± 1.61	1.34	0.0709	98.79 ± 0.88	0.98	0.1146	99.19 ± 0.82	1.58	0.3678	99.48 ± 0.69	1.89	0.6840	99.65 ± 0.42	1.65
WPBC (198 × 34)	79.85 ± 5.15	0.15	0.4901	80.35 ± 6.11	0.13	0.6146	83.12 ± 4.22	0.20	0.7236	82.87 ± 3.52	0.24	0.8650	82.36 ± 4.67	0.20
WDBC (569 × 30)	96.14 ± 1.03	1.27	0.0075	98.41 ± 0.87	0.91	0.6163	98.62 ± 0.95	1.32	0.7148	98.59 ± 0.89	2.04	0.8044	98.77 ± 1.05	1.71
Vertebral (310 × 6)	85.48 ± 2.28	0.36	0.4774	85.81 ± 3.44	0.27	0.6224	86.41 ± 2.54	0.42	0.8012	86.77 ± 2.58	0.53	0.8894	87.10 ± 3.68	0.51
Australian (690 × 14)	85.51 ± 1.62	2.05	0.4526	86.23 ± 2.00	1.37	0.6902	86.78 ± 1.69	2.39	0.8536	86.96 ± 1.28	3.11	0.9451	87.11 ± 1.82	2.73
BTSC (748 × 4)	77.01 ± 2.86	2.41	0.2378	78.88 ± 1.91	1.56	0.6675	79.46 ± 2.51	2.76	0.8245	79.82 ± 2.76	3.71	0.9504	79.68 ± 3.07	3.33
Tic_tac_toe (958 × 27)	98.33 ± 0.39	3.99	1	98.33 ± 0.39	2.69	1	98.33 ± 0.39	4.73	1	98.33 ± 0.39	9.37	1	98.33 ± 0.39	5.45
German (1000 × 24)	75.40 ± 2.85	4.56	0.4943	74.40 ± 1.66	2.93	0.2232	76.32 ± 1.87	5.21	0.7435	77.00 ± 1.03	9.35	0.9288	77.20 ± 2.74	8.27
CMC (1473 × 9)	77.39 ± 0.13	9.99	0.1728	77.66 ± 0.55	6.17	0.3959	78.46 ± 0.55	2.30	0.9765	78.28 ± 0.88	16.78	0.9246	78.59 ± 1.08	15.31

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