TABLE 1. Comparison of Representative Results in Different Settings.

Classifier	Mean Train Acc	Mean Test Acc	MCC (Threshold: 0.5)	Mean Precision (std)	Mean Recall (std)	Mean AUC of PR (std)	Mean AUC of ROC (std)
SVM (Linear)	1	0.655	0.457	0.667 (0.036)	0.645 (0.048)	0.674 (0.031)	0.799 (0.083)
SVM (RBF)	1	0.655	0.456	0.655 (0.018)	0.644 (0.048)	0.678 (0.034)	0.798 (0.083)
SVM (RBF) (feature selection)	0.963	0.68	0.495	0.701 (0.035)	0.663 (0.118)	0.688 (0.034)	0.822 (0.066)
Random Forest	1	0.496	0.344	0.752 (0.099)	0.473 (0.104)	0.670 (0.045)	0.795 (0.086)
Random Forest (feature selection)	1	0.570	0.404	0.737 (0.089)	0.558 (0.051)	0.691 (0.051)	0.817 (0.077)
XGBoost	1	0.623	0.406	0.647 (0.043)	0.606 (0.103)	0.646 (0.073)	0.793 (0.081)
XGBoost (feature selection)	1	0.605	0.380	0.623 (0.029)	0.598 (0.059)	0.616 (0.055)	0.770 (0.089)
AdaBoost	0.821	0.613	0.386	0.714 (0.144)	0.547 (0.287)	0.649 (0.094)	0.776 (0.135)
AdaBoost (feature selection)	0.810	0.577	0.314	0.632 (0.116)	0.528 (0.239)	0.576 (0.110)	0.726 (0.163)
DNN (feature selection)	0.715	0.641	0.473	0.679 (0.045)	0.626 (0.101)	0.755 (0.099)	0.875 (0.054)
DNN (l1 regularizer)	0.834	0.616	0.412	0.685 (0.069)	0.590 (0.109)	0.771 (0.118)	0.870 (0.027)
DNN (feature selection, l1 regularizer)	0.719	0.666	0.488	0.722 (0.084)	0.635 (0.134)	0.771 (0.092)	0.860 (0.039)