Optimal decision trees for the algorithm selection problem : integer programming based approaches.

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorVilas Boas, Matheus Guedes-
Autor(es): dc.creatorSantos, Haroldo Gambini-
Autor(es): dc.creatorMerschmann, Luiz Henrique de Campos-
Autor(es): dc.creatorBerghe, Greet Vanden-
Data de aceite: dc.date.accessioned2025-08-21T15:38:45Z-
Data de disponibilização: dc.date.available2025-08-21T15:38:45Z-
Data de envio: dc.date.issued2020-07-23-
Data de envio: dc.date.issued2020-07-23-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/12498-
Fonte completa do material: dc.identifierhttps://onlinelibrary.wiley.com/doi/abs/10.1111/itor.12724-
Fonte completa do material: dc.identifierhttps://doi.org/10.1111/itor.12724-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1020514-
Descrição: dc.descriptionEven though it is well known that for most relevant computational problems, different algorithms may perform better on different classes of problem instances, most researchers still focus on determining a single best algorithmic configuration based on aggregate results such as the average. In this paper, we propose integer programming-based approaches to build decision trees for the algorithm selection problem. These techniques allow the automation of three crucial decisions: (a) discerning the most important problem features to determine problem classes, (b) grouping the problems into classes, and (c) selecting the best algorithm configuration for each class. To evaluate this new approach, extensive computational experiments were executed using the linear programming algorithms implemented in the COIN-OR branch-and-cut solver across a comprehensive set of instances, including all MIPLIB benchmark instances. The results exceeded our expectations. While selecting the single best parameter setting across all instances decreased the total running time by 22%, our approach decreased the total running time by 40% on average across 10-fold cross-validation experiments. These results indicate that our method generalizes quite well and does not overfit.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectFeature-based parameter tuning-
Palavras-chave: dc.subjectDecision trees-
Palavras-chave: dc.subjectData mining-
Palavras-chave: dc.subjectVariable neighborhood search-
Palavras-chave: dc.subjectCOIN-OR branch and cut-
Título: dc.titleOptimal decision trees for the algorithm selection problem : integer programming based approaches.-
Aparece nas coleções:Repositório Institucional - UFOP

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