A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification

TL;DR

This paper presents a new robust multiclass SVM model that handles feature uncertainty using robust optimization, with linear and kernel methods validated on real datasets.

Contribution

It introduces the Twin Parametric Margin SVM with robust optimization techniques for multiclass classification under feature uncertainty.

Findings

Effective in handling data perturbations

Flexible decision functions proposed

Validated on real-world datasets with good performance

Abstract

In this paper, we introduce novel Twin Parametric Margin Support Vector Machine (TPMSVM) models designed to address multiclass classification tasks under feature uncertainty. To handle data perturbations, we construct bounded-by-norm uncertainty set around each training observation and derive the robust counterparts of the deterministic models using robust optimization techniques. To capture complex data structure, we explore both linear and kernel-induced classifiers, providing computationally tractable reformulations of the resulting robust models. Additionally, we propose two alternatives for the final decision function, enhancing models' flexibility. Finally, we validate the effectiveness of the proposed robust multiclass TPMSVM methodology on real-world datasets, showing the good performance of the approach in the presence of uncertainty.