[2603.06257] Robust support vector model based on bounded asymmetric elastic net loss for binary classification

Statistics > Machine Learning arXiv:2603.06257 (stat) This paper has been withdrawn by Haiyan Du [Submitted on 6 Mar 2026 (v1), last revised 8 Apr 2026 (this version, v2)] Title:Robust support vector model based on bounded asymmetric elastic net loss for binary classification Authors:Haiyan Du, Hu Yang View a PDF of the paper titled Robust support vector model based on bounded asymmetric elastic net loss for binary classification, by Haiyan Du and 1 other authors No PDF available, click to view other formats Abstract:In this paper, we propose a novel bounded asymmetric elastic net ($L_{baen}$) loss function and combine it with the support vector machine (SVM), resulting in the BAEN-SVM. The $L_{baen}$ is bounded and asymmetric and can degrade to the asymmetric elastic net hinge loss, pinball loss, and asymmetric least squares loss. BAEN-SVM not only effectively handles noise-contaminated data but also addresses the geometric irrationalities in the traditional SVM. By proving the violation tolerance upper bound (VTUB) of BAEN-SVM, we show that the model is geometrically well-defined. Furthermore, we derive that the influence function of BAEN-SVM is bounded, providing a theoretical guarantee of its robustness to noise. The Fisher consistency of the model further ensures its generalization capability. Since the \( L_{\text{baen}} \) loss is non-convex, we designed a clipping dual coordinate descent-based half-quadratic algorithm to solve the non-convex optimization problem ef...