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

arXiv: https://arxiv.org/abs/2603.06257 · HTML: https://mlsanity.com/paper/2603.06257/

## Key facts

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- First posted on arXiv 6 Mar 2026.

## Abstract (arXiv)

In this paper, we propose a novel bounded asymmetric elastic net (Lbaen) loss function and combine it with the support vector machine (SVM), resulting in the BAEN-SVM. The Lbaen 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 ( Lbaen ) 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.