On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19

Sandra Benítez-Peña, Emilio Carrizosa, Vanesa Guerrero, M. Dolores Jiménez-Gamero, Belén Martín-Barragán, Cristina Molero-Río, Pepa Ramírez-Cobo, Dolores Romero Morales*, M. Remedios Sillero-Denamiel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Pages (from-to)648-663
Number of pages16
JournalEuropean Journal of Operational Research
Issue number2
Early online date18 Apr 2021
Publication statusPublished - 1 Dec 2021

Keywords / Materials (for Non-textual outputs)

  • COVID-19
  • ensemble method
  • machine learning
  • mathematical optimization
  • selective sparsity


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