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Table 3 Results on datasets

From: Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

Dataset All [CI] AUC-FS [CI] EFS [CI] AUC-FS vs. EFS* all vs. EFS**
MI-Mortality 0.758 [0.700, 0.800] 0.757 [0.704, 0.811] 0.776 [0.725, 0.826] 0.228 0.201
Fibrosis 0.493 [0.300, 0.600] 0.681 [0.537, 0.824] 0.746 [0.617, 0.874] 0.273 0.018
FLIP 0.759 [0.600, 0.900] 0.723 [0.582, 0.863] 0.761 [0.633, 0.890] 0.254 0.971
SPECTF 0.807 [0.700, 0.900] 0.856 [0.811, 0.901] 0.865 [0.821, 0.910] 0.444 4.68e-4
Sonar 0.792 [0.700, 0.900] 0.840 [0.787, 0.894] 0.862 [0.813, 0.911] 0.200 0.009
WBC 0.611 [0.600, 0.700] 0.987 [0.977, 0.998] 0.991 [0.981, 1.000] 0.020 1.21e-41
  1. Column 1 to 3 are AUCs values of all features, selected by AUC-FS and by the EFS with confidential intervalls in brackets. The last two columns show the p-values of the comparison by the method of [28]. The function compares the AUC of the ROC curves of (*) the AUC-FS and EFS method and (**) of all parameters and EFS outcome. Statistical significant p-values are printed in bold