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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

 ACCSENSPEPPVNPVAUCTimeNfeats
GARS0.730.830.720.260.970.8111 min 41 s7
RFE0.570.330.60.090.880.542 s10
SBF0.730.830.720.260.970.8720 s83
rfGA0.710.660.2610.922 h 33 min145
svmGA0.680.830.660.230.970.8616 h 53 min94
LASSO0.660.830.640.220.970.801 s2
  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