Sparse Reject Option Classifier Using Successive Linear Programming
This work addresses the need for efficient and interpretable classification with rejection capabilities, though it appears incremental as it builds on existing loss functions and optimization techniques.
The paper tackles the problem of learning sparse reject option classifiers by proposing a method using double ramp loss and DC programming, achieving comparable performance to state-of-the-art methods while successfully learning sparse classifiers.
In this paper, we propose an approach for learning sparse reject option classifiers using double ramp loss $L_{dr}$. We use DC programming to find the risk minimizer. The algorithm solves a sequence of linear programs to learn the reject option classifier. We show that the loss $L_{dr}$ is Fisher consistent. We also show that the excess risk of loss $L_d$ is upper bounded by the excess risk of $L_{dr}$. We derive the generalization error bounds for the proposed approach. We show the effectiveness of the proposed approach by experimenting it on several real world datasets. The proposed approach not only performs comparable to the state of the art but it also successfully learns sparse classifiers.