RT journal article
T1 The soft-margin Support Vector Machine with ordered weighted average
A1 Marín, Alfredo
A1 Martínez Merino, Luisa Isabel
A1 Puerto, Justo
A1 Rodríguez Chía, Antonio Manuel
A2 Estadística e Investigación Operativa
K1 Data science
K1 Mixed integer quadratic programming
K1 OWA operators
K1 Support Vector Machine
AB This paper deals with a cost sensitive extension of the standard Support Vector Machine (SVM) using an ordered weighted sum of the deviations of misclassified individuals with respect to their corresponding supporting hyperplanes. In contrast with previous heuristic approaches, an exact method that applies the ordered weighted average operator in the classical SVM model is proposed. Specifically, when weights are sorted in non-decreasing order, a quadratic continuous formulation is developed. For general weights, a mixed integer quadratic formulation is proposed. In addition, our results prove that nonlinear kernel functions can be also applied to these new models extending its applicability beyond the linear case. Extensive computational results reported in the paper show that the predictive performance provided by the proposed exact solution approaches are better than the ones provided by the classical models (linear and nonlinear kernel) and similar or better than the previous ones provided by the heuristic solution by Maldonado et al. (2018). © 2021 The Authors
PB ELSEVIER
SN 0950-7051
YR 2022
FD 2022
LK http://hdl.handle.net/10498/25968
UL http://hdl.handle.net/10498/25968
LA eng
NO Alfredo Marín has been supported by Spanish Ministry of Science and Innovation under project PID2019-110886RB-I00 . Part of this research was conducted while he was on sabbatical at Universidad de Sevilla, Spain . Luisa I. Martínez-Merino, Antonio M. Rodríguez-Chía and Justo Puerto thank the Agencia Estatal de Investigación (AEI) and the European Regional Development’s funds (ERDF) : project PID2020-114594GB-C21/C22 ; Regional Government of Andalusia : projects FEDER-UCA18-106895 , FEDER-US-1256951 and P18-FR-1422 ; and Fundación BBVA : project NetmeetData (Ayudas Fundación BBVA a equipos de investigación científica 2019). Luisa I. Martínez-Merino has been supported by Subprograma Juan de la Cierva Formación 2019 ( FJC2019-039023-I ) and PAIDI 2020 postdoctoral fellowship financed by European Social Fund and Junta de Andalucía
DS Repositorio Institucional de la Universidad de Cádiz
RD 12-nov-2026