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We consider the problem of learning reject option classifiers. The goodnessof a reject option classifier is quantified using $0-d-1$ loss function whereina loss $d \in (0,.5)$ is assigned for rejection. In this paper, we propose {\emdouble ramp loss} function which gives a continuous upper bound for $(0-d-1)$loss. Our approach is based on minimizing regularized risk under the doubleramp loss using {\em difference of convex (DC) programming}. We show theeffectiveness of our approach through experiments on synthetic and benchmarkdatasets. Our approach performs better than the state of the art reject optionclassification approaches.

Double Ramp Loss Based Reject Option Classifier