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

• URL: http://arxiv.org/abs/2603.06257v1
• Date: Fri, 06 Mar 2026 13:17:36 GMT
• Title: Robust support vector model based on bounded asymmetric elastic net loss for binary classification
• Abstract summary: We propose a novel bounded elastic net ($L_baen$) loss function combine it with the support vector machine (SVM)<n>By proving violation tolerance upper bound bound (VTUB) of BAEN-SVM, we show that the model is geometrically well-defined.<n>We derive that the influence function of BAEN-SVM is bounded, providing a theoretical guarantee of its robustness.
• Score: 4.4568150591571785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 efficiently. Experimental results on artificial and benchmark datasets indicate that the proposed method outperforms classical and advanced SVMs, particularly in noisy environments.

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