In this paper, we present novel Twin Parametric Margin Support Vector Machine (TPMSVM) models to tackle the problem of multiclass classification. We explore the cases of linear and nonlinear classifiers and propose two possible alternatives for the final decision function. Since real-world observations are plagued by measurement errors and noise, data uncertainties need to be considered in the optimization models. For this reason, we construct bounded-by-norm uncertainty sets around each sample and derive the robust counterpart of deterministic models by means of robust optimization techniques. Finally, we test the proposed TPMSVM methodology on real-world datasets, showing the good performance of the approach.
View on arXiv@article{leone2025_2306.06213,
title={ A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification },
author={ Renato De Leone and Francesca Maggioni and Andrea Spinelli },
journal={arXiv preprint arXiv:2306.06213},
year={ 2025 }
}