01277nas a2200121   4500008004100000245003900041210003800080260000900118520092900127100002001056700002401076856005501100       2023                        eng d00aAUC-based Selective Classification0 aAUCbased Selective Classification  bPMLR3 aSelective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.1 aPugnana, Andrea1 aRuggieri, Salvatore  uhttps://proceedings.mlr.press/v206/pugnana23a.html