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

DOI10.1016/j.ejor.2021.04.016zbMath1489.62237OpenAlexW3153487720MaRDI QIDQ2239910

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

Publication date: 5 November 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2021.04.016

zbMATH Keywords

machine learningmathematical optimizationCOVID-19ensemble methodselective sparsity

Mathematics Subject Classification ID

Epidemiology (92D30) Linear inference, regression (62J99) Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05)

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Uses Software

• Gurobi
• GitHub
• ConstrainedLasso
• inTrees

Cites Work

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