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Table 4 Comparative analysis with respect to competing feature selection / extraction techniques using the same classifier (LDA)

From: A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection

Exp.

Preprocessing

Feature Selection / Extraction

Dimensionality of the resulted feature space

Training (%)

Testing (%)

Execution time (s)

8.

Fuzzification

Vec2im – Siam

1

98.69

86.64

113.736

9.

Normalization

Vec2im – Siam

1

90.20

80.64

113.245

10.

Normalization

Wr-FS (best feature)

1

82.84

77.91

1.991

11.

Fuzzification

Wr-FS (best feature)

1

82.84

77.91

1.953

12.

Normalization

Wr-FS (2 first features)

2

90.79

82.04

3.048

13.

Fuzzification

Wr-FS (2 first features)

2

90.79

82.04

3.161

14.

Normalization

Wr-FS (3 first features)

3

92.20

82.98

4.370

15

Fuzzification

Wr-FS (3 first features)

3

91.24

85.81

4.788

16.

Normalization

Wr-FS (10 first features)

10

92.33

84.06

17.091

17.

Fuzzification

Wr-FS (10 first features)

10

92.03

85.93

17.214

18.

Normalization

ILFS

39

95.44

78.44

0.307

19.

Normalization

InfFS

13

93.91

81.94

0.063

20.

Normalization

Relief-F

13

92.96

81.75

122.105

21.

Normalization

LS

33

95.42

78.50

158.381

22.

Normalization

Fisher

13

93.91

81.94

0.415

23.

Normalization

CFS

24

93.14

81.51

0.071

24.

Normalization

UFSOL

39

95.44

78.44

312.436

25.

Normalization

Lasso

22

93.61

82.04

14.505

26.

Normalization

Chi2

33

95.44

78.48

1.113

27.

Normalization

mRMR

26

94.70

81.24

1.060

28.

Normalization

FuzCoC

25

94.61

81.94

1.160

29.

Normalization

PCA (1 pr. component)

1

89.69

75.64

0.144

30.

Normalization

PCA (2 pr. components)

2

89.47

76.30

31.

Normalization

PCA (3 pr. components)

3

89.48

76.32