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Table 7 Accuracy of classifiers and the number of genes used in classifiers (in parenthesis) for the independent test set for multi-class expression datasets

From: TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection

Method

Leuk1

Lung1

Leuk2

SRBCT

Breast

Lung2

DLBCL

Leuk3

Cancers

GCM

Aver

TSG

97.06

(6)

81.25

(20)

100

(44)

100

(13)

86.67

(63)

95.52

(60)

93.33

(16)

91.07

(95)

79.73

(81)

67.39

(112)

89.20

HC-TSP*

97.06

(4)

71.88

(4)

80

(4)

95

(6)

66.67

(8)

83.58

(8)

83.33

(10)

77.68

(12)

74.32

(20)

52.17

(26)

78.17

HC-k-TSP*

97.06

(36)

78.13

(20)

100

(24)

100

(30)

66.67

(24)

94.03

(28)

83.33

(46)

82.14

(64)

82.43

(128)

67.39

(134)

85.12

DT*

85.29

(2)

78.13

(4)

80

(2)

75

(3)

73.33

(4)

88.06

(5)

86.67

(5)

75.89

(16)

68.92

(10)

52.17

(18)

76.35

NB*

85.29

81.25

100

60

66.67

88.06

86.67

32.14

79.73

52.17

73.2

k-NN*

67.65

75

86.67

30

63.33

88.06

93.33

75.89

64.86

34.78

67.96

PAM*

97.06

(44)

78.13

(13)

93.33

(62)

95

(285)

93.33

(4822)

100

(614)

90

(3949)

93.75

(3338)

87.84

(2008)

56.52

(1253)

88.5

1-vs-1-SVM*

79.41

87.5

100

100

83.33

97.01

100

84.82

83.78

65.22

88.11

  1. *Results reported in Tan et al. [3]Results obtained with our method (TSG) : NB, k-NN, 1-vs-1-SVM used entire set of genes in classification. That is, 7129, 7129, 12582, 2308, 9216, 12600, 4026, 12558, 12533, 16063 for the ten data sets respectively.