Double Ramp Loss Based Reject Option Classifier

We consider the problem of learning reject option classifiers. The goodness of a reject option classifier is quantified using 0-d-1 loss function wherein a loss d ∈ (0,.5) is assigned for rejection. In this paper, we propose em double ramp loss function which gives a continuous upper bound for (0-d-1) loss. Our approach is based on minimizing regularized risk under the double ramp loss using em difference of convex (DC) programming. We show the effectiveness of our approach through experiments on synthetic and benchmark datasets. Our approach performs better than the state of the art reject option classification approaches.