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arXiv · 1311.6556

Double Ramp Loss Based Reject Option Classifier

Naresh Manwani·Kalpit Desai·Sanand Sasidharan·Ramasubramanian Sundararajan
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Abstract

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.

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Naresh Manwani, Kalpit Desai, Sanand Sasidharan, Ramasubramanian Sundararajan. 2014-12-08. Double Ramp Loss Based Reject Option Classifier. https://doi.org/10.1007/978-3-319-57454-7_53

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