Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches

By Matheus Guedes Vilas Boas, Haroldo Gambini Santos, L. Merschmann, Greet Vanden Berghe. 2019

22 citations recorded by the source corpus.

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 automate three crucial decisions: (i) discerning the most important problem features to determine problem classes; (ii) grouping the problems into classes and (iii) select 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 & 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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems, Mathematical Optimization

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Three major groups of these techniques are (1) mathematical programming, including mixed integer programming (MIP) (see the works of Bennett and Blue, 1996; Rudin and Ertekin, 2018; Verwer and Zhang, 2019; Vilas Boas et al., 2019; Menickelly et al., 2018; Aghaei et al., 2020) and SAT solvers (Narody”
Generalized and Scalable Optimal Sparse Decision Trees (2020, methodology): “,1992; Bennett & Blue ,1996) to the present (Verwer & Zhang 2019 ;Blanquero et al. ,2018 Nijssen et al. 2020 Bertsimas & Dunn ,2017 ;Rudin & Ertekin 2018 Menickelly et al. ,2018;Vilas Boas et al. 2019), but these generic solvers tend to be slow. A common way to speed them up is to ma”