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Table 2 Performance metrics of different ML algorithms using negative samples generated through random (top) and K-means (bottom) sampling

From: Improved cytokine–receptor interaction prediction by exploiting the negative sample space

 

SE

SP

ACC

MCC

AUC

g-means

Random (loocv)

 NB

52.3

59.3

55.8

0.119

0.554

54.9

 A1DE

70.5

53.3

62.9

0.264

0.707

62.5

 SMO-RBF

79.1

74.5

76.7

0.536

0.767

76.7

 SMO-PolyK

62.8

50.9

56.9

0.199

0.569

56.4

 SMO-PuK

73.0

83.5

78.2

0.568

0.782

78.0

 IBK

81.4

71.9

76.6

0.536

0.766

76.4

 Bagging

66.6

63.5

65.1

0.303

0.718

65.0

 RF

73.2

71.6

72.4

0.449

0.807

72.3

K-means (loocv)

 NB

73.3

51.3

61.3

0.253

0.713

63.0

 A1DE

83.6

84.1

83.9

0.679

0.908

83.8

 SMO-RBF

90.2

90.8

90.5

0.814

0.905

90.5

 SMO-PolyK

80.5

87.9

84.2

0.686

0.842

84.0

 SMO-PuK

85.7

91.7

88.4

0.770

0.887

88.3

 IBK

86.1

86.1

86.1

0.722

0.861

86.0

 Bagging

86.4

92.3

89.4

0.789

0.945

89.3

 RF

89.7

96.3

93.0

0.862

0.965

92.9

  1. The averages of ten runs of loocv are reported. The best performance for each metric is shown in italic