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Table 4 Classification accuracies (%) on 9 real data sets

From: Feature weight estimation for gene selection: a local hyperlinear learning approach

Method

Datasets

Average

 

CNS

Colon

DLBCL

GCM

Leukemia

Lung

Prostate1

Prostate2

Prostate3

 

TSP [20]

77.90

91.10

98.10

75.40

93.80

98.30

95.10

67.60

97.00

88.26

k-TSP [19]

97.10

90.30

97.40

85.40

95.83

98.90

91.18

75.00

97.00

92.01

PAM [19]

82.35

89.52

85.45

82.32

94.03

97.90

90.89

81.25

94.24

88.66

sumdiff-PAM [21]

79.41

87.10

87.01

83.57

95.83

98.34

93.14

77.27

96.97γ

88.74

mul-PAM [21]

85.29

90.32

92.21

82.86

95.83

98.90

92.16

79.55

93.94

90.12

sign-PAM [21]

85.29

88.71

94.81

81.07

95.83

98.90

90.20

76.14

100

90.11

HBE [22]

  

96.10

 

98.61

 

96.08

   

IVGA-SVM [23]

 

91.61

  

97.22

 

92.06

   

BBF-SVM [24]

 

87.10

92.71

   

94.12

   

SVM [25]

82.35

83.87

96.10

93.57

98.61

98.90

91.18

76.14

100

91.19

NB [25]

79.41

58.06

79.22

82.5

98.61

98.34

62.75

80.68

93.94

81.50

BMSF-SVM [25]

94.12

95.16

97.40

98.57

98.61

99.45

97.06

98.86

100

97.69

BMSF-LDA [25]

97.06

87.10

96.10

90.36

98.61

97.79

95.10

94.32

96.97η

94.82

BMSF-QDA [25]

97.06

90.32

94.81

90.36

97.22

97.23

94.12

90.91

100

94.67

BMSF-NB [25]

94.12

87.10

88.31

87.86

95.83

98.90

89.22

89.77

100

92.34

LHR-SVMξ

100

87.10

94.81

100

98.61

100

96.08

95.45

100

96.89

LHR-LDAξ

99.47

87.38

95.00

99.44

98.75

99.47

97.09

95.42

99.47

96.83

LHR-NBξ

97.79

90.32

92.21

97.24

98.61

97.24

98.04

89.77

97.79

95.44

LHR-KNNξ

98.45

91.94

96.10

91.00

100

100

99.02

94.32

99.45

96.70

LHR-HKNNξ

100

90.32

97.40

97.40

100

100

97.06

94.32

100

97.39

I-RELIEF-SVMη[12]

83.43

75.81

92.21

92.21

94.44

83.98

88.24

82.95

81.12

86.04

I-RELIEF-LDAη[12]

81.17

74.05

89.46

89.46

92.86

80.06

80.64

87.50

80.18

83.93

I-RELIEF-NBη[12]

85.08

67.74

84.42

84.42

91.67

86.74

73.53

81.82

87.29

82.52

I-RELIEF-KNNη[12]

88.4

82.26

96.10

96.10

94.44

88.40

91.18

86.36

87.85

90.12

I-RELIEF-HKNNη[12]

83.98

77.42

96.10

96.10

95.83

86.16

85.29

77.27

83.98

86.90

  1. ξClassification with our selected genes.
  2. ηClassification with selected genes by [11].
  3. γThe value of 96.97 in [26] could have been rounded to 97.00 and is suboptimal.
  4. The optimal and suboptimal values on each tested data are highlighted in bold and italic, respectively. The averaged performance of the proposed method with HKNN classifier is suboptimal to BMSM-SVM by a neglectable difference. Besides, the averaged performance of LHR, coupling with five classifiers show a dramatically smaller variance (0.725) than other BMSM does (2.191), thus implying a high capability of stability with respect to classification models.