Pruning an ensemble of classifiers via reinforcement learning

Neurocomputing

Volumn 72, Issue 7-9, 2009, Pages 1900-1909

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

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Ensemble selection; Reinforcement learning

Indexed keywords

COMBINATION METHODS; CRITICAL DOMAINS; ENSEMBLE OF CLASSIFIERS; ENSEMBLE SELECTION; EXPERIMENTAL COMPARISONS; OPTIMAL POLICIES; Q-LEARNING ALGORITHMS;

CLASSIFIERS; EDUCATION; LEARNING ALGORITHMS; REINFORCEMENT LEARNING;

REINFORCEMENT;

ACCURACY; ARTICLE; CLASSIFIER; DATA BASE; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; POLICY; PRIORITY JOURNAL; STATISTICAL MODEL;

EID: 61849098236     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.06.007     Document Type: Article

References (38)

1. D.N.A. Asuncion, UCI machine learning repository, 2007. URL 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉.
2. Banfield R.E., Hall L.O., Bowyer K.W., and Kegelmeyer W.P. Ensemble diversity measures and their application to thinning. Inf. Fusion 6 1 (2005) 49-62
3. Breiman L. Bagging predictors. Mach. Learn. 24 2 (1996) 123-140
4. Caruana R., Niculescu-Mizil A., Crew G., and Ksikes A. Ensemble selection from libraries of models. Proceedings of the 21st International Conference on Machine learning, ICML' 04. Banff, Alberta, Canada (2004)
5. Demsar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (2006) 1-30
6. Dietterich T.G. Machine-learning research: four current directions. AI Mag. 18 4 (1998) 97-136
7. T.G. Dietterich, Ensemble methods in machine learning, in: Proceedings of the 1st International Workshop on Multiple Classifier Systems, MCS '00, 2000.
8. Dimitrakakis C., and Bengio S. Online adaptive policies for ensemble classifiers. Trends Neurocomput. 64 (2005) 211-221
9. Dzeroski S., and Zenko B. Is combining classifiers with stacking better than selecting the best one?. Mach. Learn. 54 3 (2004) 255-273
10. Fan W., Chu F., Wang H., and Yu P.S. Pruning and dynamic scheduling of cost-sensitive ensembles. Proceedings of the 19th National Conference on Artificial Intelligence. Edmonton, Alberta, Canada (2002)
11. Friedman M. A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Statist. 11 (1940) 86-92
12. Giacinto G., and Roli F. An approach to the automatic design of multiple classifier systems. Pattern Recognition Lett. 22 1 (2001) 25-33
13. Iman R.L., and Davenport J.M. Approximations of the critical region of the Friedman statistic. Commun. Stat. (1980) 571-595
14. Kaelbling L.P., Littman M.L., and Moore A.P. Reinforcement learning: a survey. J. Artif. Intell. Res. 4 (1996) 237-285
15. Lagoudakis M.G., and Littman M.L. Algorithm selection using reinforcement learning. Proceedings of the 17th International Conference on Machine Learning (2000)
16. A. Lazarevic, Z. Obradovic, Effective pruning of neural network classifiers, in: 2001 IEEE/INNS International Conference on Neural Networks, IJCNN 2001, 2001.
17. Margineantu D., and Dietterich T. Pruning adaptive boosting. Proceedings of the 14th International Conference on Machine Learning (1997)
18. Martinez-Munoz G., and Suarez A. Aggregation ordering in bagging. International Conference on Artificial Intelligence and Applications (IASTED) (2004), Acta Press
19. Martinez-Munoz G., and Suarez A. Pruning in ordered bagging ensembles. 23rd International Conference in Machine Learning (ICML-2006) (2006), ACM Press, New York
20. Martinez-Munoz G., and Suarez A. Using boosting to prune bagging ensembles. Pattern Recognition Lett. 28 1 (2007) 156-165
21. P.B. Nemenyi, Distribution-free multiple comparisons, Ph.D. Thesis, Princeton University, 1963.
22. Partalas I., Tsoumakas G., Katakis I., and Vlahavas I. Ensemble pruning using reinforcement learning. Proceedings of the 4th Hellenic Conference on Artificial Intelligence (SETN 2006). Heraklion, Greece (2006)
23. Prodromidis A., and Chan P. Meta-learning in distributed data mining systems: issues and approaches. In: Kargupta H., and Chan P. (Eds). Advances of Distributed Data Mining (2000), MIT, AAAI Press, Cambridge, MA
24. Prodromidis A., and Stolfo S.J. Cost complexity-based pruning of ensemble classifiers. Knowl. Inf. Syst. 3 4 (2001) 449-469
25. Provost F., and Kolluri V. A survey of methods for scaling up inductive algorithms. Data Mining Knowl. Discovery 3 2 (1999) 131-169
26. Schapire R.E. The strength of weak learnability. Mach. Learn. 5 (1990) 197-227
27. Street W.N., and Kim Y. A streaming ensemble algorithm (SEA) for large-scale classification. 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)
28. Sutton R.S., and Barto A.G. Reinforcement Learning, An Introduction (1999), MIT Press, Cambridge, MA
29. Tamon C., and Xiang J. On the boosting pruning problem. 11th European Conference on Machine Learning (ECML 2000) (2000), Springer, Berlin
30. Tsoumakas G., Angelis L., and Vlahavas I. Selective fusion of heterogeneous classifiers. Intelligent Data Anal. 9 6 (2005) 511-525
31. Tsoumakas G., Katakis I., and Vlahavas I. Effective voting of heterogeneous classifiers. Proceedings of the 15th European Conference on Machine Learning (ECML 2004). Pisa, Italy (2004)
32. Tsoumakas G., and Vlahavas I. Distributed data mining of large classifier ensembles. 2nd Hellenic Conference on Artificial Intelligence (2002)
33. Wang H., Fan W., Yu P.S., and Han J. Mining concept-drifting data streams using ensemble classifiers. 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003), ACM, New York, NY, USA
34. Watkins C., and Dayan P. Q-learning. Mach. Learn. 8 (1992) 279-292
35. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques. second ed. (2005), Morgan Kaufmann, Los Altos, CA
36. Wolpert D.H. Stacked generalization. Neural Networks 5 (1992) 241-259
37. Zhang Y., Burer S., and Street W.N. Ensemble pruning via semi-definite programming. J. Mach. Learn. Res. 7 (2006) 1315-1338
38. Zhou Z., and Tang W. Selective ensemble of decision trees. Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2003. Chongqing, China (2003)