Abstract
The automatic tuning of the parameters of algorithms and automatic selection of algorithms has received a lot of attention recently. One possible approach is the use of machine learning techniques to learn classifiers which, given the characteristics of a particular problem, make a decision as to which algorithm or what parameters to use. Little research has been done into which machine learning algorithms are suitable and the impact of picking the “right” over the “wrong” technique. This paper investigates the differences in performance of several techniques on different data sets. It furthermore provides evidence that by using a meta-technique which combines several machine learning algorithms, we can avoid the problem of having to pick the “best” one and still achieve good performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009)
Bain, S., Thornton, J., Sattar, A.: Evolving Variable-Ordering heuristics for constrained optimisation. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 732–736. Springer, Heidelberg (2005)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn. 36(1-2), 105–139 (1999)
Chen, F., Jin, R.: Active algorithm selection. In: AAAI, pp. 534–539 (2007)
Dechter, R.: Constraint Processing. Elsevier Science, Amsterdam (2003)
Dietterich, T.G.: Ensemble methods in machine learning. In: First International Workshop on Multiple Classifier Systems, pp. 1–15 (2000)
Epstein, S., Freuder, E., Wallace, R., Morozov, A., Samuels, B.: The adaptive constraint engine. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 525–542. Springer, Heidelberg (2002)
Gent, I., Jefferson, C., Miguel, I.: Minion: A fast scalable constraint solver. In: ECAI, pp. 98–102 (2006)
Gent, I.P., Miguel, I., Moore, N.C.A.: Lazy explanations for constraint propagators. In: Carro, M., Peña, R. (eds.) PADL 2010. LNCS, vol. 5937, pp. 217–233. Springer, Heidelberg (2010)
Gent, I., Miguel, I., Nightingale, P.: Generalised arc consistency for the alldifferent constraint: An empirical survey. Artif. Intell. 172(18), 1973–2000 (2008)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009)
KhudaBukhsh, A., Xu, L., Hoos, H., Leyton-Brown, K.: SATenstein: Automatically building local search SAT solvers from components. In: IJCAI, pp. 517–524 (2009)
Kohavi, R.: A study of Cross-Validation and bootstrap for accuracy estimation and model selection. In: IJCAI, p. 1137–1143 (1995)
Lavesson, N., Davidsson, P.: Quantifying the impact of learning algorithm parameter tuning. In: AAAI, pp. 395–400 (2006)
McKay, B.: Practical graph isomorphism. In: Numerical Mathematics and Computing, pp. 45–87 (1981)
Minton, S.: Automatically configuring constraint satisfaction programs: A case study. Constraints 1(1/2), 7–43 (1996)
O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using case-based reasoning in an algorithm portfolio for constraint solving. In: Irish Conf. on AI (2008)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Rice, J.: The algorithm selection problem. Adv. Computers 15, 65–118 (1976)
Weerawarana, S., Houstis, E.N., Rice, J.R., Joshi, A., Houstis, C.E.: PYTHIA: a knowledge-based system to select scientific algorithms. ACM Trans. Math. Softw. 22(4), 447–468 (1996)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2005)
Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: Portfolio-based algorithm selection for SAT. JAIR 32, 565–606 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kotthoff, L., Miguel, I., Nightingale, P. (2010). Ensemble Classification for Constraint Solver Configuration. In: Cohen, D. (eds) Principles and Practice of Constraint Programming – CP 2010. CP 2010. Lecture Notes in Computer Science, vol 6308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15396-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-15396-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15395-2
Online ISBN: 978-3-642-15396-9
eBook Packages: Computer ScienceComputer Science (R0)