TSVM-M3: Twin support vector machine based on multi-order moment matching for large-scale multi-class classification

For multi-class classification, many existing methods, such as multiple weighted linear loss twin support vector machine (MWLTSVM), construct multiple decision hyperplanes by minimizing the positive points loss's first-order moment (mean), which may lead to sensitivity to outliers. Also, when faced with a large-scale classification problem, how to speed up the process of solving the optimization model is also a challenge. An alternative is to use rectangular kernel technology (RKT) to reduce computational complexity. However, RKT is based on the uniform point selection method, which can be proven to be ineffective in improving classifier performance. To address these problems, a novel classifier under the structure of one-versusrest for multi-class classification is proposed in this paper, named twin support vector machine based on multi-order moment matching (TSVM-M-3). When constructing the decision hyperplanes, TSVM-M-3 takes the first-order and second-order moments (mean and variance) of positive points loss into consideration and implements this by introducing an adjusting factor into the objective function. A theoretical analysis of the robustness of the proposed TSVM-M-3 is also provided. Meanwhile, a novel RKT based on the density-dependent data selection method is proposed for large-scale classification. We demonstrate that the proposed RKT can benefit from reducing modeling error. Experimental results show the effectiveness of the proposed TSVM-M-3. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a novel twin support vector machine, TSVM-M-3, for multi-class classification and a new RKT for large-scale classification. TSVM-M-3 considers the first and second-order moments of positive points loss and introduces an adjusting factor when constructing decision hyperplanes; RKT uses a density-dependent data selection method to reduce modeling error.