RT Journal Article
T1 A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification
A1 Jiménez-Cordero, María Asunción
A1 Morales-González, Juan Miguel
A1 Pineda-Morente, Salvador
K1 Matemáticas aplicadas
AB In recent years, feature selection has become a challenging problem in several machine learning fields, such as classification problems. Support Vector Machine (SVM) is a well-known technique applied in classification tasks. Various methodologies have been proposed in the literature to select the most relevant features in SVM. Unfortunately, all of them either deal with the feature selection problem in the linear classification setting or propose ad-hoc approaches that are difficult to implement in practice. In contrast, we propose an embedded feature selection method based on a min-max optimization problem, where a trade-off between model complexity and classification accuracy is sought. By leveraging duality theory, we equivalently reformulate the min-max problem and solve it without further ado using off-the-shelf software for nonlinear optimization. Theefficiency and usefulness of our approach are tested on several benchmark data sets in terms of accuracy, number of selected features and interpretability.
PB Elsevier
YR 2021
FD 2021
LK https://hdl.handle.net/10630/32672
UL https://hdl.handle.net/10630/32672
LA spa
NO Asunción Jiménez-Cordero, Juan Miguel Morales, Salvador Pineda, A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification, European Journal of Operational Research, Volume 293, Issue 1, 2021, Pages 24-35, ISSN 0377-2217, https://doi.org/10.1016/j.ejor.2020.12.009. (https://www.sciencedirect.com/science/article/pii/S0377221720310195)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 2 mar 2026