Robust relative margin support vector machines

Abstract

Recently, a class of classifiers, called relative margin machine, has been developed. Relative margin machine has shown significant improvements over the large margin counterparts on real-world problems. In binary classification, the most widely used loss function is the hinge loss, which results in the hinge loss relative margin machine. The hinge loss relative margin machine is sensitive to outliers. In this article, we proposed to change maximizing the shortest distance used in relative margin machine into maximizing the quantile distance, the pinball loss which is related to quantiles was used in classification. The proposed method is less sensitive to noise, especially the feature noise around the decision boundary. Meanwhile, the computational complexity of the proposed method is similar to that of the relative margin machine.