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Table 5 Performance comparison of the LHR with 12 standard feature selection schemes (FSSs)

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

Classifier

FSS

DLBCL

Prostate1

GCM

Prostate2

CNS

Leukemia

Prostate3

Colon

Lung

Avg.

 

No. genes

27

22

6

24

7

23

6

18

5

 
 

IG

93.5

96.1

81.8

84.1

88.2

97.2

100

87.1

99.4

91.9

 

TR

94.8

97.1

82.1

84.1

85.3

97.2

100

91.9

99.4

92.4

 

Gini

93.5

95.1

80.7

81.8

88.2

95.8

100

83.9

99.4

90.9

 

SumM

94.8

93.1

81.8

70.5

85.3

98.6

100

88.7

98.9

90.2

 

MaxM

94.8

97.1

82.1

84.1

85.3

97.2

100

91.9

99.4

92.4

SVMη

SumV

94.8

97.1

82.1

84.1

85.3

97.2

100

91.9

99.4

92.4

 

t-stat

92.2

91.2

83.2

81.8

82.4

95.8

100

87.1

98.9

90.3

 

OSVM

98.7

93.1

80.7

73.9

85.3

95.8

72.7

85.5

98.3

87.1

 

MIDξ

75.3

75.3

75.3

75.3

73.5

65.3

72.7

64.5

82.9

73.4

 

MIQξ

75.3

75.3

75.3

75.3

73.5

65.3

72.7

64.5

82.9

73.4

 

I-RELIEF

92.2

88.2

81.2

83.0

83.4

94.4

81.2

75.8

84.0

84.8

 

LHR

94.8

96.1

100

95.5

100

98.6

100

87.1

100

96.9

 

LOGO

100

100

88.9

92.0

97.1

100

100

91.9

100

96.7

 

IG

88.2

90.1

82.9

79.6

88.3

92.0

100

80.5

97.3

88.8

 

TR

88.0

90.1

82.9

80.3

86.7

93.0

100

82.6

97.8

89.0

 

Gini

79.5

90.2

83.6

79.9

88.3

94.5

100

82.6

98.3

88.5

 

SumM

86.1

92.1

82.5

71.9

89.2

91.6

100

79.3

98.3

87.9

 

MaxM

90.7

94.2

82.9

80.7

84.2

94.3

100

80.7

97.8

89.5

LDA

SumV

90.9

91.2

82.1

84.0

88.3

92.9

100

74.0

97.3

89.0

 

t-stat

77.5

89.4

83.9

84.2

82.5

91.6

97.5

80.5

93.4

86.7

 

OSVM

97.4

92.3

79.6

84.4

85.0

92.9

40.0

82.4

98.9

83.7

 

MIDξ

75.5

83.9

81.6

83.9

90.8

78.9

84.2

77.6

96.7

83.7

 

MIQξ

76.3

79.3

78.2

72.9

73.3

85.9

83.3

78.8

95.6

80.4

 

I-RELIEF

89.5

80.6

80.7

87.5

81.2

92.9

80.2

74.0

80.1

83.0

 

LHR

95.0

97.1

99.4

95.4

99.5

98.8

99.5

87.4

99.5

96.8

 

LOGO

98.6

98.0

90.0

95.6

86.7

100

100

86.9

100

95.1

 

IG

88.3

93.1

80.0

84.1

91.2

95.8

100

87.1

98.9

90.9

 

TR

89.6

93.1

80.0

84.1

88.2

95.8

100

88.7

98.9

90.9

 

Gini

89.6

92.2

80.4

83.0

91.2

95.8

100

88.7

98.9

91.1

 

SumM

89.6

92.2

80.7

73.9

91.2

95.8

100

88.7

98.9

90.1

 

MaxM

89.6

93.1

80.0

84.1

88.2

95.8

100

88.7

98.9

90.9

NB

SumV

89.6

93.1

80.0

84.1

88.2

95.8

100

88.7

98.9

90.9

 

t-stat

89.6

94.1

82.5

83.0

91.2

98.6

100

79.0

98.3

90.7

 

OSVM

90.9

94.1

81.1

81.8

91.2

95.8

100

83.9

98.3

90.8

 

MIDξ

76.6

76.6

80.5

75.3

88.2

84.7

84.8

80.6

97.8

82.8

 

MIQξ

80.5

83.1

77.9

79.2

73.5

94.4

84.8

74.2

97.2

82.8

 

I-RELIEF

84.4

73.5

87.3

81.8

85.1

91.7

87.3

67.7

86.7

82.8

 

LHR

92.2

98.0

97.2

89.8

97.8

98.6

97.8

90.3

97.2

95.4

 

LOGO

98.7

93.1

84.3

94.3

97.1

100

100

90.3

100

95.3

 

IG

92.2

96.1

85.7

84.1

91.2

98.6

100

88.7

98.9

92.8

 

TR

90.9

98.0

84.6

84.1

88.2

98.6

100

87.1

98.9

92.3

 

Gini

90.9

92.2

86.1

84.1

88.2

98.6

100

85.5

98.9

91.6

 

SumM

93.5

92.2

84.3

86.4

94.1

98.6

100

87.1

98.9

92.8

 

MaxM

90.9

98.0

84.6

84.1

88.2

98.6

100

87.1

98.9

92.3

KNNη

SumV

90.9

98.0

84.6

84.1

88.2

98.6

100

87.1

98.9

92.3

 

t-stat

93.5

94.1

86.8

86.4

91.2

97.2

100

88.7

99.4

93.0

 

OSVM

90.9

93.1

87.9

80.7

91.2

94.4

84.8

85.5

98.9

89.7

 

MID ξ

88.3

89.6

90.9

87.0

85.3

90.3

93.9

77.4

91.2

88.2

 

MIQ ξ

93.5

87.0

87.0

89.6

85.3

91.7

93.9

79.0

91.2

88.7

 

I-RELIEF

96.1

91.2

87.8

86.4

88.4

94.4

87.8

82.3

88.4

89.2

 

LHR

96.1

99.0

100

94.3

99.4

100

99.4

91.9

100

97.8

 

LOGO

100

99.0

94.6

96.6

94.1

100

100

91.9

100

97.4

 

IG

90.9

97.1

83.9

85.2

91.2

95.8

100

83.9

98.9

91.9

 

TR

90.9

95.1

82.5

85.2

88.2

97.2

100

87.1

98.9

91.7

 

Gini

90.9

93.1

84.3

84.1

94.1

98.6

100

87.1

98.9

92.3

 

SumM

92.2

91.2

83.9

84.1

91.2

98.6

100

87.1

100

92.0

 

MaxM

90.9

95.1

82.5

85.2

88.2

97.2

100

87.1

98.9

91.7

HKNN η

SumV

90.9

95.1

82.5

85.2

88.2

97.2

100

87.1

98.9

91.7

 

t-stat

89.6

91.2

81.4

81.8

94.1

97.2

100

83.9

99.4

91.0

 

OSVM

89.6

92.2

83.9

79.5

91.2

97.2

87.9

87.1

99.4

89.8

 

MID ξ

80.5

81.8

87.0

83.1

79.4

84.7

90.9

79.0

95.0

84.6

 

MIQ ξ

88.3

83.1

80.5

89.6

82.4

91.7

90.9

75.8

93.9

86.2

 

I-RELIEF

96.1

85.3

84.0

77.3

84.0

95.8

84.0

77.4

86.2

85.6

 

LHR

97.4

97.1

100

94.3

100

100

100

90.3

100

97.7

 

LOGO

100

99.0

96.8

96.6

97.1

100

100

91.9

100

97.9

  1. ξPreprocessing of the data via t-test with confidence leel of 0.01 to reduce the computation burden on estimating of mutual information.
  2. ηHyper-parameters are estimated via 5-fold cross validation.
  3. The number of genes is determined by LHR and used for all other FSSs. LOOCV criteria is used to evaluate the performance of the FSSs, coupling with five classification models. The optimal and suboptimal accuracy (columnwise) on each tested data are highlighted in bold and italic, respectively.