Ensemble classification for constraint solver configuration

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6308 LNCS, Issue , 2010, Pages 321-329

  Kotthoff, Lars ^a   Miguel, Ian ^a   Nightingale, Peter ^a  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAMMING; CONSTRAINT THEORY; LEARNING SYSTEMS; PARAMETER ESTIMATION;

AUTOMATIC SELECTION; AUTOMATIC TUNING; CONSTRAINT SOLVERS; ENSEMBLE CLASSIFICATION; MACHINE LEARNING TECHNIQUES;

LEARNING ALGORITHMS;

EID: 78149262600     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15396-9_27     Document Type: Conference Paper

References (22)

1. 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)
2. 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)
3. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn. 36(1-2), 105-139 (1999)
4. Chen, F., Jin, R.: Active algorithm selection. In: AAAI, pp. 534-539 (2007)
5. Dechter, R.: Constraint Processing. Elsevier Science, Amsterdam (2003)
6. Dietterich, T.G.: Ensemble methods in machine learning. In: First International Workshop on Multiple Classifier Systems, pp. 1-15 (2000)
7. 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)
8. Gent, I., Jefferson, C., Miguel, I.: Minion: A fast scalable constraint solver. In: ECAI, pp. 98-102 (2006)
9. 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)
10. Gent, I., Miguel, I., Nightingale, P.: Generalised arc consistency for the alldifferent constraint: An empirical survey. Artif. Intell. 172(18), 1973-2000 (2008)
11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009)
12. KhudaBukhsh, A., Xu, L., Hoos, H., Leyton-Brown, K.: SATenstein: Automatically building local search SAT solvers from components. In: IJCAI, pp. 517-524 (2009)
13. Kohavi, R.: A study of Cross-Validation and bootstrap for accuracy estimation and model selection. In: IJCAI, p. 1137-1143 (1995)
14. Lavesson, N., Davidsson, P.: Quantifying the impact of learning algorithm parameter tuning. In: AAAI, pp. 395-400 (2006)
15. McKay, B.: Practical graph isomorphism. In: Numerical Mathematics and Computing, pp. 45-87 (1981)
16. Minton, S.: Automatically configuring constraint satisfaction programs: A case study. Constraints 1(1/2), 7-43 (1996)
17. 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)
18. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
19. Rice, J.: The algorithm selection problem. Adv. Computers 15, 65-118 (1976)
20. 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)
21. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2005)
22. Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: Portfolio-based algorithm selection for SAT. JAIR 32, 565-606 (2008)