RT journal article
T1 A robust SVM-based approach with feature selection and outliers detection for classification problems
A1 Baldomero Naranjo, Marta
A1 Martínez Merino, Luisa Isabel
A1 Rodríguez Chía, Antonio Manuel
A2 Estadística e Investigación Operativa
K1 Data Science
K1 Classification
K1 Support Vector Machine
K1 Outliers Detection
K1 Feature selection
K1 Mixed Integer Programming
AB This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic approach is validated by comparing the quality of the solutions provided by this approach with the exact approach. In addition, the classifiers obtained with the heuristic method are tested and compared with existing SVM-based models to demonstrate their efficiency.
PB Elsevier
SN 0957-4174
YR 2021
FD 2021
LK http://hdl.handle.net/10498/31346
UL http://hdl.handle.net/10498/31346
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026