A Fast and Robust TSVM for Pattern Classification

TL;DR

This paper introduces FR-TSVM, a fast and robust twin support vector machine that uses fuzzy membership and an efficient coordinate descent algorithm to improve speed and robustness in pattern classification tasks.

Contribution

It proposes a novel FR-TSVM model with fuzzy input weighting and an efficient optimization algorithm, enhancing speed and robustness over traditional TSVMs.

Findings

FR-TSVM achieves faster training times.

FR-TSVM demonstrates improved robustness to noisy data.

Experimental results outperform existing TSVM methods.

Abstract

Twin support vector machine~(TSVM) is a powerful learning algorithm by solving a pair of smaller SVM-type problems. However, there are still some specific issues such as low efficiency and weak robustness when it is faced with some real applications. In this paper, we propose a Fast and Robust TSVM~(FR-TSVM) to deal with the above issues. In order to alleviate the effects of noisy inputs, we propose an effective fuzzy membership function and reformulate the TSVMs such that different input instances can make different contributions to the learning of the separating hyperplanes. To further speed up the training procedure, we develop an efficient coordinate descent algorithm with shirking to solve the involved a pair of quadratic programming problems (QPPs). Moreover, theoretical foundations of the proposed model are analyzed in details. The experimental results on several artificial and benchmark datasets indicate that the FR-TSVM not only obtains a fast learning speed but also shows a robust classification performance. Code has been made available at: https://github.com/gaobb/FR-TSVM.