RT journal article
T1 On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19
A1 Benítez-Peña, Sandra
A1 Carrizosa, Emilio
A1 Guerrero, Vanesa
A1 Jiménez-Gamero, M. Dolores
A1 Martín-Barragán, Belén
A1 Molero-Río, Cristina
A1 Ramírez Cobo, Josefa
A1 Romero Morales, Dolores
A1 Sillero-Denamiel, M. Remedios
A2 Estadística e Investigación Operativa
K1 Machine Learning
K1 Ensemble Method
K1 Mathematical Optimization
K1 Selective Sparsity
K1 COVID-19
AB Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with low accuracy, and it may be too complex to understand and explain. This paper proposes and studies a novel Mathematical Optimization model to build a sparse ensemble, which trades off the accuracy of the ensemble and the number of base regressors used. The latter is controlled by means of a regularization term that penalizes regressors with a poor individual performance. Our approach is flexible to incorporate desirable properties one may have on the ensemble, such as controlling the performance of the ensemble in critical groups of records, or the costs associated with the base regressors involved in the ensemble. We illustrate our approach with real data sets arising in the COVID-19 context. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
PB ELSEVIER
SN 0377-2217
YR 2021
FD 2021-12
LK http://hdl.handle.net/10498/25655
UL http://hdl.handle.net/10498/25655
LA eng
NO We thank the reviewers for their thorough comments and suggestions, which have been very valuable to strengthen the quality of the paper. This research has been financed in part by research projects EC H2020 MSCA RISE NeEDS (Grant agreement ID: 822214); FQM-329 and P18-FR-2369 (Junta de Andalucia, Spain); MTM2017-89422-P (Ministerio de Economia, Industria y Competitividad, Spain); PID2019-110886RB-I00 (Ministerio de Ciencia, Innovacin y Universidades, Spain); PR2019-029 (Universidad de Cadiz, Spain); PITUFLOW-CM-UC3M (Comunidad de Madrid and Universidad Carlos III de Madrid, Spain); and EP/R00370X/1 (EPSRC, United Kingdom). This support is gratefully acknowledged.
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026