A mathematical programming approach to SVM-based classification with label noise Blanco Izquierdo, Víctor Japón, Alberto Puerto, Justo Supervised classification SVM Mixed integer non linear programming Label noise The authors of this research acknowledge financial support by the Spanish Ministerio de Ciencia y Tecnologia, Agencia Estatal de Investigacion and Fondos Europeos de Desarrollo Regional (FEDER) via project PID2020114594GB-C21. The authors also acknowledge partial support from projects FEDER-US-1256951, Junta de Andalucía P18-FR-1422, CEI-3-FQM331, NetmeetData: Ayudas Fundación BBVA a equipos de investigación científica 2019. The first author was also supported by projects P18-FR-2369 (Junta de Andalucía) and IMAG-Maria de Maeztu grant CEX2020-001105-M /AEI /10.13039/501100011033. (Spanish Ministerio de Ciencia y Tecnologia). In this paper we propose novel methodologies to optimally construct Support Vector Machine-based classifiers that take into account that label noise occur in the training sample. We propose different alternatives based on solving Mixed Integer Linear and Non Linear models by incorporating decisions on relabeling some of the observations in the training dataset. The first method incorporates relabeling directly in the SVM model while a second family of methods combines clustering with classification at the same time, giving rise to a model that applies simultaneously similarity measures and SVM. Extensive computational experiments are reported based on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of the proposed approaches. 2022-10-13T12:39:59Z 2022-10-13T12:39:59Z 2022-08-30 journal article V. Blanco et al. A mathematical programming approach to SVM-based classification with label noise. Computers & Industrial Engineering 172 (2022) 108611 [https://doi.org/10.1016/j.cie.2022.108611] https://hdl.handle.net/10481/77303 10.1016/j.cie.2022.108611 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier