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Table 3 Effects of sequentially adding data sources (continued)

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

Data sources MKLdiv-dc MKLdiv-conv SimpleMKL MKL-RKDA
SW1 62.92 62.92 62.40 61.87
SW1S 65.27
(24.72 s)
66.31
(10.49 s)
64.22
(40.60 s)
64.75
(0.12 s)
SW1SW2S 67.10
(48.79 s)
66.05
(4.65 s)
64.75
(61.71 s)
64.49
(0.15 s)
SW1SW2CS 73.36
(40.65 s)
72.32
(23.43 s)
65.01
(62.81 s)
67.62
(0.17 s)
SW1SW2CSH 74.67
(72.19 s)
72.32
(8.69 s)
66.31
(75.11 s)
67.88
(0.15 s)
SW1SW2CSHP 74.93
(123.98 s)
74.41
(11.63 s)
66.31
(74.85 s)
69.71
(0.18 s)
SW1SW2CSHPZ 75.19
(189.91 s)
73.36
(15.00 s)
68.92
(109.09 s)
66.05
(0.20 s)
SW1SW2CSHPZV 74.41
(278.47 s)
74.41
(17.47 s)
66.31
(117.14 s)
69.19
(0.25 s)
SW1SW2CSHPZVL1 73.10
(404.82 s)
73.32
(49.41 s)
66.84
(101.01 s)
68.66
(0.25 s)
SW1SW2CSHPZVL1L4 72.84
(576.29 s)
72.06
(57.83 s)
67.10
(107.88 s)
67.62
(0.25 s)
SW1SW2CSHPZVL1L4L14 72.58
(748.72 s)
72.36
(19.43 s)
66.84
(163.85 s)
69.19
(0.28 s)
SW1SW2CSHPZVL1L4L14L30 73.36
(811.54 s)
71.01
(83.93 s)
66.57
(197.57 s)
68.40
(0.31 s)
  1. Test set accuracy of sequentially adding fold discriminatory data sources (continued) according to the ranking of kernel weights obtained by MKLdiv-dc over all data sources. The results of the Bayesian kernel learning method were not employed in [21], hence we do not list in the table. 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.