In this paper a method defining a reject option applicable to a given 0-reject classifier is proposed. The reject option is based on an estimate of the classification reliability, measured by a reliability evaluator . Trivially, once a reject threshold has been fixed, a sample is rejected if the corresponding value of is below . Obviously, as represents the least tolerable classification reliab...

We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow’s rule, is defined by two thresholds on posterior probabilities. From simple desiderata, namely the consistency and the sparsity of the classifier, we derive the double hinge loss function that focuses on estimating cond...

We consider multi-label classification problems in application scenarios where classifier accu-racy is not satisfactory, but manual annotation is too costly. In single-label problems, a wellknown solution consists of using a reject option, i.e., allowing a classifier to withhold unreliabledecisions, leaving them (and only them) to human operators. We argue that this solution can be<...