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Table 1 Average classification accuracy and standard deviation.

From: ANMM4CBR: a case-based reasoning method for gene expression data classification

# Iteration

10

20

30

40

50

Leukemia

BW+k NN

95.7 ± 1.2

96.9 ± 1.8

96.6 ± 2.2

96.6 ± 1.2

96.8 ± 1.7

 

MRMR+k NN

96.5 ± 2.5

96.4 ± 2.1

97.4 ± 1.7

96.9 ± 2.2

95.8 ± 2.4

 

BW+SVM

95.6 ± 1.3

95.7 ± 1.7

95.9 ± 2.2

96.2 ± 2.3

96.9 ± 1.2

 

MRMR+SVM

96.4 ± 2.5

96.8 ± 3.6

97.6 ± 2.0

97.1 ± 2.7

96.8 ± 3.4

 

LogitBoost

95.3 ± 2.9

96.0 ± 2.4

96.6 ± 1.8

96.6 ± 2.8

96.7 ± 1.7

 

ANMM4CBR

96.3 ± 2.4

97.5 ± 1.7

97.3 ± 1.8

96.6 ± 1.7

97.0 ± 2.3

Colon

BW+k NN

81.2 ± 8.1

82.8 ± 7.5

83.5 ± 4.2

83.4 ± 5.3

83.6 ± 6.5

 

MRMR+k NN

83.7 ± 4.3

83.6 ± 7.9

84.2 ± 6.0

83.8 ± 5.9

83.5 ± 6.9

 

BW+SVM

84.0 ± 4.3

83.6 ± 6.4

83.6 ± 6.0

84.2 ± 7.2

84.5 ± 7.9

 

MRMR+SVM

85.4 ± 5.8

84.1 ± 6.6

84.0 ± 4.0

84.6 ± 7.0

84.7 ± 8.1

 

LogitBoost

84.4 ± 4.3

84.5 ± 8.9

83.6 ± 4.9

84.2 ± 6.8

84.1 ± 4.6

 

ANMM4CBR

86.3 ± 6.1

86.7 ± 5.6

86.2 ± 4.2

86.5 ± 5.6

85.6 ± 4.4

SRBCT

BW+k NN (50)

94.4 ± 4.2

97.7 ± 2.1

97.9 ± 1.3

98.2 ± 1.6

98.0 ± 1.2

 

MRMR+k NN (50)

78.4 ± 9.0

97.4 ± 1.9

98.6 ± 1.0

98.8 ± 0.9

98.2 ± 0.8

 

BW+SVM (97)

94.0 ± 3.2

98.0 ± 1.4

98.4 ± 1.2

98.8 ± 0.9

99.2 ± 0.3

 

MRMR+SVM (95)

81.0 ± 10.5

98.2 ± 1.0

98.9 ± 1.3

99.1 ± 0.7

99.2 ± 0.2

 

LogitBoost (102)

94.9 ± 3.1

97.3 ± 1.8

98.0 ± 1.6

98.6 ± 1.1

98.6 ± 0.6

 

ANMM4CBR (50)

90.3 ± 5.5

97.3 ± 1.5

98.8 ± 1.2

99.3 ± 0.7

99.7 ± 0.3

GCM

BW+k NN (50)

46.2 ± 4.7

47.4 ± 7.0

51.2 ± 4.9

52.6 ± 6.2

54.1 ± 5.8

 

MRMR+k NN (50)

41.1 ± 7.1

42.7 ± 8.1

51.5 ± 1.6

58.3 ± 4.9

60.5 ± 5.9

 

BW+SVM (254)

53.7 ± 5.1

58.1 ± 9.8

59.0 ± 6.6

66.6 ± 6.7

66.9 ± 3.6

 

MRMR+SVM (259)

51.0 ± 7.7

60.3 ± 7.0

61.8 ± 3.7

64.8 ± 8.2

67.8 ± 4.6

 

LogitBoost (273)

57.1 ± 4.9

60.1 ± 1.9

60.6 ± 4.0

62.1 ± 5.7

65.1 ± 5.4

 

ANMM4CBR (50)

41.1 ± 1.2

51.0 ± 8.1

57.2 ± 6.9

61.1 ± 1.4

63.3 ± 3.9

  1. Each experiment was carried out for 100 runs. The best results in different situations are labeled as black. Here the iteration number means the number of features used by each single classifier. In OVA case, the total number of genes may exceed the iteration number, since in OVA a multiclass problem is solved by considering many binary ones. In the parentheses we list the average number of features selected by each method when the iteration number is 50. See Table 2 for another experiment on multiclass data set.