  The goal of classification with rejection is to avoid risky misclassification
in error-critical applications such as medical diagnosis and product
inspection. In this paper, based on the relationship between classification
with rejection and cost-sensitive classification, we propose a novel method of
classification with rejection by learning an ensemble of cost-sensitive
classifiers, which satisfies all the following properties: (i) it can avoid
estimating class-posterior probabilities, resulting in improved classification
accuracy, (ii) it allows a flexible choice of losses including non-convex ones,
(iii) it does not require complicated modifications when using different
losses, (iv) it is applicable to both binary and multiclass cases, and (v) it
is theoretically justifiable for any classification-calibrated loss.
Experimental results demonstrate the usefulness of our proposed approach in
clean-labeled, noisy-labeled, and positive-unlabeled classification.
