Benítez Peña, SandraCarrizosa Priego, Emilio JoséGuerrero, VanesaJiménez Gamero, María DoloresMartín Barragán, BelénMolero Río, CristinaRamírez Cobo, JosefaRomero Morales, María DoloresSillero Denamiel, María Remedios2024-09-242024-09-242021-12-01Benítez Peña, S., Carrizosa Priego, E.J., Guerrero, V., Jiménez Gamero, M.D., Martín Barragán, B., Molero Río, C.,...,Sillero Denamiel, M.R. (2021). On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19. European Journal of Operational, 295 (2), 648-663. https://doi.org/10.1016/j.ejor.2021.04.016.0377-22171872-6860https://hdl.handle.net/11441/162780Since 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.application/pdf16 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Machine LearningEnsemble MethodMathematical OptimizationSelective SparsityCOVID-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1016/j.ejor.2021.04.016