Twin K-class support vector classification with pinball loss

The twin K-class support vector classification (TKSVC) is an effective and efficient algorithm for multiclass classification problem. However, due to the use of hinge loss function, it is very sensitive to feature noises and not stable for re-sampling. To avoid the above flaws, we present a novel twin K-class support vector classification with pinball loss (Pin-TKSVC) in this paper. The structure of Pin-TKSVC is analogous to that of TKSVC. Namely, it adopts "One-vs.-One-vs.-Rest'' structure to do multi-class classification and solves two smaller sized quadratic programming problems to save the running time. Besides, the introduction of pinball loss does not increase the additional computational complexity. More importantly, the Pin-TKSVC maximizes the quantile distance between different categories, making it a more robust classifier. To testify its performance, we do numerical experiments on twenty datasets with different noises, and compare it with five state-of-the-art algorithms. The experimental results confirm the validity of our algorithm. (C) 2021 Elsevier B.V. All rights reserved.