@misc{10481/77303,
year = {2022},
month = {8},
url = {https://hdl.handle.net/10481/77303},
abstract = {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.},
organization = {Spanish Ministerio de Ciencia y Tecnologia, Agencia Estatal de Investigacion and Fondos
Europeos de Desarrollo Regional (FEDER) via project PID2020114594GB-C21},
organization = {FEDER-US-1256951},
organization = {Junta de Andalucía P18-FR-1422},
organization = {CEI-3-FQM331},
organization = {NetmeetData: Ayudas Fundación BBVA a equipos de investigación científica 2019},
organization = {Project P18-FR-2369 Junta de Andalucía},
organization = {IMAG-Maria de Maeztu grant CEX2020-001105-M /AEI /10.13039/501100011033. (Spanish
Ministerio de Ciencia y Tecnologia)},
publisher = {Elsevier},
keywords = {Supervised classification},
keywords = {SVM},
keywords = {Mixed integer non linear programming},
keywords = {Label noise},
title = {A mathematical programming approach to SVM-based classification with label noise},
doi = {10.1016/j.cie.2022.108611},
author = {Blanco Izquierdo, Víctor and Japón, Alberto and Puerto, Justo},
}