Double Ramp Loss Based Reject Option Classifier
This work addresses the need for improved reject option classification, which is important for applications requiring reliable decision-making, but it appears incremental as it builds on existing loss functions and optimization methods.
The paper tackles the problem of learning reject option classifiers by proposing a double ramp loss function that provides a continuous upper bound for the 0-d-1 loss, and the approach outperforms state-of-the-art methods in experiments on synthetic and benchmark datasets.
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 $d \in (0,.5)$ 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.