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Table 2 Effects of sequentially adding data sources

From: Enhanced protein fold recognition through a novel data integration approach

Data sources MKLdiv-dc MKLdiv-conv VBKC SimpleMKL MKL-RKDA
C 51.69 51.69 51.2 ± 0.5 51.69 47.25
CS 56.39
(20.23 s)
55.35
(0.32 s)
55.7 ± 0.5
(-)
55.61
(14.67 s)
48.30
(0.15 s)
CSH 57.70
(50.35 s)
58.22
(2.44 s)
57.7 ± 0.6
(-)
56.91
(10.40 s)
55.61
(0.12 s)
CSHP 58.48
(39.02 s)
53.52
(72.14 s)
57.9 ± 0.9
(-)
57.96
(17.84 s)
56.65
(0.18 s)
CSHPV 60.05
(75.05 s)
53.26
(86.39 s)
58.1 ± 0.8
(-)
57.96
(15.05 s)
55.87
(0.17 s)
CSHPVZ 59.26
(135.08 s)
53.52
(99.64 s)
58.6 ± 1.1
(-)
59.00
(20.02 s)
57.70
(0.20 s)
CSHPVZL1 60.05
(221.75 s)
52.74
(122.74 s)
60.0 ± 0.8
(-)
61.35
(27.38 s)
57.70
(0.21 s)
CSHPVZL1L4 62.14
(315.70 s)
52.74
(129.08 s)
60.8 ± 1.1
(-)
61.61
(151.38 s)
58.22
(0.25 s)
CSHPVZL1L4L14 62.14
(450.57 s)
61.09
(57.09 s)
61.5 ± 1.2
(-)
60.05
(42.81 s)
59.53
(0.25 s)
CSHPVZL1L4L14L30 62.14
(612.72 s)
62.14
(67.29 s)
62.2 ± 1.3
(-)
62.40
(64.74 s)
55.61
(0.25 s)
CSHPVZL1L4L14L30SW1 71.80
(620.16 s)
71.54
(17.97 s)
66.4 ± 0.8
(-)
65.79
(78.94 s)
66.84
(0.31 s)
CSHPVZL1L4L14L30SW1SW2 73.36
(805.11 s)
71.01
(84.21 s)
68.1 ± 1.2
(-)
66.57
(196.42 s)
68.40
(0.31 s)
SHPVZL1L4L14L30 60.57
(438.89 s)
61.09
(67.92 s)
61.1 ± 1.4
(-)
59.00
(44.79 s)
54.56
(0.25 s)
  1. The result of Bayesian kernel learning model (VBKC) is cited from [21]. The results not employed there are denoted by '-'. The term inside the parenthesis is the CPU running time (seconds). The best test set accuracy of each kernel learning method is marked in bold.