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Table 2 Performance evaluation, testing FS methods on the ‘binary mid-dimension’ dataset

From: GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets

 

ACC

SEN

SPE

PPV

NPV

AUC

Time

Nfeats

GARS

0.73

0.83

0.72

0.26

0.97

0.81

11 min 41 s

7

RFE

0.57

0.33

0.6

0.09

0.88

0.54

2 s

10

SBF

0.73

0.83

0.72

0.26

0.97

0.87

20 s

83

rfGA

0.7

1

0.66

0.26

1

0.92

2 h 33 min

145

svmGA

0.68

0.83

0.66

0.23

0.97

0.86

16 h 53 min

94

LASSO

0.66

0.83

0.64

0.22

0.97

0.80

1 s

2

  1. ACC Accuracy, SEN Sensitivity, SPE Specificity, PPV Positive Predictive Value, NPV Negative Predictive Value, AUC Area Under ROC Curve, Time average learning time for each cross-validation fold, Nfeats n. of selected features
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BMC Bioinformatics

ISSN: 1471-2105

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