Variable selection in AUC-optimizing classification

Optimizing the receiver operating characteristic (ROC) curve is a popular way to evaluate a binary classifier under imbalanced scenarios frequently encountered in practice. A practical approach to constructing a linear binary classifier is presented by simultaneously optimizing the area under the ROC curve (AUC) and selecting informative variables in high dimensions. In particular, the smoothly clipped absolute deviation (SCAD) penalty is employed, and its oracle property is established, which enables the development of a consistent BIC-type information criterion that greatly facilitates the tuning procedure. Both simulated and real data analyses demonstrate the promising performance of the proposed method in terms of AUC optimization and variable selection.