Ensemble pruning using reinforcement learning

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

Volumn 3955 LNAI, Issue , 2006, Pages 301-310

  Partalas, Ioannis ^a   Tsoumakas, Grigorios ^a   Katakis, Ioannis ^a   Vlahavas, Ioannis ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; HEURISTIC METHODS; MATHEMATICAL MODELS; STATISTICAL METHODS;

CLASSIFIER COMBINATION; META-LEARNING LEVEL; REINFORCEMENT LEARNING;

LEARNING SYSTEMS;

EID: 33745807978     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11752912_31     Document Type: Conference Paper

References (19)

1. Dietterich, T.G.: Machine-learning research: Four current directions. The AI Magazine 18 (1998) 97-136
2. Dietterich, T.G.: Ensemble methods in machine learning. Lecture Notes in Computer Science 1857 (2000) 1-15
3. Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Effective voting of heterogeneous classifiers. In: Proceedings of the 15th European Conference on Machine Learning, ECML 04. (2004) 465-476
4. Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: ICML '04: Proceedings of the twenty-first international conference on Machine learning, New York, NY, USA, ACM Press (2004) 18
5. Button, R.S., Barto, A.G.: Reinforcmement Learning, An Introduction. MIT Press (1999)
6. Watkins, C., Dayan, P.: Q-learning. Machine Learning 8 (1992) 279-292
7. Wolpert, D.H.: Stacked generalization. Technical Report LA-UR-90-3460, Los Alamos, NM (1990)
8. Christos Dimitrakakis, S.B.: Online adaptive policies for ensemble classifiers. Trends in Neurocomputing 64 (2005) 211-221
9. Lagoudakis, M.G., Littman, M.L.: Algorithm selection using reinforcement learning. In: Proc. 17th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA (2000) 511-518
10. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd Edition. Morgan Kaufmann (2005)
11. Kohavi, R.: The power of decision tables. In Lavrac, N., Wrobel, S., eds.: Proceedings of the European Conference on Machine Learning. Lecture Notes in Artificial Intelligence 914, Berlin, Heidelberg, New York, Springer Verlag (1995) 174-189
12. Cohen, W.W.: Fast effective rule induction. In Prieditis, A., Russell, S., eds.: Proc. of the 12th International Conference on Machine Learning, Tahoe City, CA, Morgan Kaufmann (1995) 115-123
13. Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proc. 15th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA (1998) 144-151
14. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)
15. Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6 (1991) 37-66
16. Cleary, J.G., Trigg, L.E.: K*: an instance-based learner using an entropic distance measure. In: Proc. 12th International Conference on Machine Learning, Morgan Kaufmann (1995) 108-114
17. John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. (1995) 338-345
18. Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford, UK (1996)
19. D.J. Newman, S. Hettich, C.B., Merz, C.: UCI repository of machine learning databases (1998)