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Title: Optimal decision trees for the algorithm selection problem : integer programming based approaches.
Authors: Vilas Boas, Matheus Guedes
Santos, Haroldo Gambini
Merschmann, Luiz Henrique de Campos
Berghe, Greet Vanden
Keywords: Feature-based parameter tuning
Decision trees
Data mining
Variable neighborhood search
COIN-OR branch and cut
Issue Date: 2019
Citation: VILAS BOAS, M. G. et al. Optimal decision trees for the algorithm selection problem: integer programming based approaches. International Transactions in Operational Research, set. 2019. Disponível em: <>. Acesso em: 18 jun. 2020.
Abstract: Even 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.
ISSN: 1475-3995
Appears in Collections:DECOM - Artigos publicados em periódicos

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