GL-TSVM: A Robust and Smooth Twin Support Vector Machine with Guardian Loss Function

Abstract

Twin support vector machine (TSVM), a variant of support vector machine (SVM), has garnered significant attention due to its 3/4 times lower computational complexity compared to SVM. However, due to the utilization of the hinge loss function, TSVM is sensitive to outliers or noise. To remedy it, we introduce the guardian loss (G-loss), a novel loss function distinguished by its asymmetric, bounded, and smooth characteristics. We then fuse the proposed G-loss function into the TSVM and yield a robust and smooth classifier termed GL-TSVM. Further, to adhere to the structural risk minimization (SRM) principle and reduce overfitting, we incorporate a regularization term into the objective function of GL-TSVM. To address the optimization challenges of GL-TSVM, we devise an efficient iterative algorithm. The experimental analysis on UCI and KEEL datasets substantiates the effectiveness of the proposed GL-TSVM in comparison to the baseline models. Moreover, to showcase the efficacy of the proposed GL-TSVM in the biomedical domain, we evaluated it on the breast cancer (BreaKHis) and schizophrenia datasets. The outcomes strongly demonstrate the competitiveness of the proposed GL-TSVM against the baseline models. The supplementary file, along with the source code for the proposed GL-TSVM model, is publicly accessible at https://github.com/mtanveer1/GL-TSVM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.