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Table 23 The proposed methods compared with the hybrid of Ridge regression and RFE

From: Effective hybrid feature selection using different bootstrap enhances cancers classification performance

Datasets

Train

Data %

Test

Data %

Over-fitting

Diff. %

Pre

Rec

F1-score

NO.F

F-Time

(sec)

C-Time

(sec)

AUC

Var.

ACC

%

SVM classifier

 RNA gene

100.000

99.627

0.373

0.998

0.830

0.831

10,265

10,160.720

2.962

1.000

0.000036

99.627

 DNA CNV

93.446

80.761

12.685

0.772

0.707

0.710

8190

37,302.17

3.216

0.944

0.000527

80.761

 Parkinson’s

disease

100.000

82.930

17.070

0.810

0.714

0.738

376.000

5.482

1.195

0.855

0.003410

82.930

 Dermatology

diseases

99.727

94.805

4.922

0.950

0.947

0.944

13.000

0.016

0.080

0.994

0.001556

94.805

 BreastEW

100.000

93.675

6.325

0.941

0.926

0.932

15.000

0.0159

0.101

0.984

0.000969

93.675