RT journal article
T1 Ordered Weighted Average Support Vector Regression
A1 Martínez Merino, Luisa Isabel
A1 Puerto, Justo
A1 Rodríguez Chía, Antonio Manuel
A2 Estadística e Investigación Operativa
K1 Support vector regression
K1 Ordered Weighted Average operators
K1 Mixed integer quadratic programming
K1 Goodness of fitting
AB This paper introduces a novel Support Vector Regression (SVR) model that incorporates Ordered Weighted Average (OWA) operators to differently penalize deviations of observations from the ɛ-strip. The penalty is determined based on the position of each deviation in the ordered vector of all deviations. A key contribution of this work is the development of two nonlinear formulations: a continuous formulation for non-decreasing monotone weight vectors and a mixed-integer formulation for general weight vectors. By leveraging dual approaches associated with these formulations, the model accommodates nonlinear kernel functions. To enhance computational efficiency, two heuristic approaches are proposed for deriving suitable regression hyperplanes in reduced computation times, as compared to exact methods. Additionally, a third heuristic approach is developed to handle nonlinear kernel functions effectively. Computational experiments conducted on both real and synthetic datasets demonstrate that the exact formulations yield remarkable regression functions with respect to the following standard metrics: mean absolute errors (MAE) and mean squared errors (MSE). The heuristic approaches are shown to be particularly efficient for larger datasets, striking a balance between solution quality and computational effort.
PB Elsevier
SN 0957-4174
YR 2025
FD 2025
LK http://hdl.handle.net/10498/36650
UL http://hdl.handle.net/10498/36650
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-nov-2026