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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.