A robust twin parametric margin support vector machine for multiclass classification

What the paper says

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. • A novel Twin Parametric Margin Support Vector Machine model is proposed. • The model is designed to tackle multiclass classification tasks. • The model is protected from uncertainty via robust optimization techniques. • Both linear and nonlinear classifiers are considered. • The effectiveness of the proposed robust model is validated on real-world datasets.