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Table 5 Performance in accuracy.

From: A new approach to enhance the performance of decision tree for classifying gene expression data

Method

GE1

GE2

GE3

GE4

GE5

GE6

GE7

BVROC-Tree

66.85 ± 3.26

84.16 ± 0.02

98.9 ± 0.90

52.38 ± 15.06

89.95 ± 3.31

97.57 ± 1.33

77.97 ± 0.03

ROC-Tree

64.13 ± 4.53

86.26 ± 0.05

98.34 ± 0.89

38.10 ± 5.95

88.24 ± 2.33

94.44 ± 2.96

52.63 ± 0.07

AUCsplit

56.96 ± 0.09

81.93 ± 0.02

96.14 ± 1.36

34.01 ± 2.87

82.47 ± 3.96

81.61 ± 3.28

50.53 ± 0.07

C4.5

53.48 ± 5.67

78.04 ± 1.83

93.21 ± 1.07

41.7 ± 4.74

79.42 ± 5.45

84.39 ± 2.01

39.00 ± 5.48

ADTree

55.22 ± 5.87

89.89 ± 2.80

95.14 ± 2.17

43.10 ± 4.80

86.76 ± 2.63

88.82 ± 5.06

49.00 ± 4.18

REPTree

58.26 ± 2.83

78.64 ± 2.99

95.01 ± 1.79

44.23 ± 5.18

80.88 ± 3.33

87.64 ± 4.49

57.00 ± 13.51

Random Tree

51.74 ± 1.82

65.53 ± 3.24

92.03 ± 5.62

46.40 ± 6.74

62.50 ± 5.23

81.64 ± 11.47

47.00 ± 16.43

Random Forest

48.6 ± 4.85

81.45 ± 4.62

92.98 ± 5.36

47.52 ± 7.19

80.88 ± 2.56

82.13 ± 10.33

43.00 ± 10.37

Naïve Bayes

50.60 ± 5.82

88.60 ± 2.26

93.85 ± 5.27

46.15 ± 7.44

55.88 ± 4.76

84.85 ± 11.26

62.00 ± 4.47

k-NN

47.10 ± 5.31

86.80 ± 2.29

93.73 ± 4.88

48.23 ± 8.61

78.68 ± 4.78

84.68 ± 10.42

44.00 ± 4.18

  1. Table representing the overall accuracy for gene expression datasets using 5 × 10 fold cross-validation scheme