| Title: |
Ensemble Pruning using Reinforcement Learning |
| Author(s): |
I. Partalas, G. Tsoumakas, I. Katakis, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (10 pages).
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| Keywords: |
Reinforcement Learning, Multiple Classifier Systems, Ensemble Prunning, Classification.
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| Appeared in: |
Proc. 4th Hellenic Conference on Artificial Intelligence (SETN-06), G. Antoniou, G. Potamias, D. Plexousakis, C. Spyropoulos (Ed.), Springer-Verlag, LNAI 3955, pp. 301-310, Heraklion, Crete, 18-20 May, 2006.
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| Abstract: |
Multiple Classifier systems have been developed in order to improve classification accuracy using methodologies for effective classifier combination. Classical approaches use heuristics, statistical tests, or a meta-learning level in order to find out the optimal combination function. We study this problem from a Reinforcement Learning perspective. In our modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment. We evaluate our approach by comparing with state-of-the-art combination methods and obtain very promising results. |
| See also : |
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This paper has been cited by the following:
| 1 |
Chan P.P.K., Xiaoqin Zeng, Tsang E.C.C., Yeung D.S., Lee J.W.T., "Neural network ensemble pruning using sensitivity measure in web applications," Proc. IEEE International Conference on Systems, Man and Cybernetics, 7-10 Oct. 2007, pp.3051-3056 |
| 2 |
D. Kalles. “Player Co-Modelling in a Strategy Board Game: Discovering how to Play Fast”, (to appear in the journal) Cybernetics and Systems, 2007 |
| 3 |
D. Kalles. “Measuring Expert Impact on Learning how to Play a Board Game”, Proc. 4th IFIP Conference on Artificial Intelligence Applications and Innovations, Athens, Greece, September, 2007. |
| 4 |
C. Dimitrakakis, "Ensembles for sequence learning", PhD Thesis, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland, 2007. |
| 5 |
Dimitris Kalles and Christos Kalantzis "Evolving Computer Game Playing via Human-Computer Interaction: Machine Learning Tools in the Knowledge Engineering Life-Cycle, in Knowledge-based Software Engineering, Maria Virvou and T. Nakamura (Eds), pages 59-68, IOS Press, 2008. |
| 6 |
Kalles, D., Kanellopoulos, P. (2008) A Minimax Tutor for Learning to Play a Board Game, Proc. ECAI ’08 Workshop on Artificial Intelligence in Games, Patras, Greece, pp. 10-14 |
| 7 |
Hernandez-Lobato, D. (2009) Prediction Based on Averages over Automatically Induced Learners: Ensemble Methods and Bayesian Techniques, PhD Thesis, Computer Science Department, Autonomous University of Madrid. |
| 8 |
Martinez-Munoz, G., Hernandez-Lobato, D., Suarez, A. (2009) An analysis of Ensemble Pruning Techniques Based on Ordered Agrregation, IEEE Transactions on Pattern Analysis and Machine Intelligence, February 2009 (vol. 31 no. 2) pp. 245-259.
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| 9 |
Mohamed Farouk Abdel Hady (2010) Semi-Supervised Learning with Committees: Exploiting Unlabeled Data Using Ensemble Learning Algorithms, PhD Thesis, Faculty of Engineering and Computer Science at University of Ulm, Germany, 2010 |
| 10 |
An, K., Meng, J. (2010) Voting-Averaged Combination Method for Regressor Ensemble, Advanced Intelligent Computing Theories and Applications, Lecture Notes in Computer Science, 2010, Volume 6215/2010, 540-546
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| 11 |
Ávila, J.L., Gibaja, E.L., Zafra, A., Ventura, S. (2011) A gene expression programming algorithm for multi-label classification, Journal of Multiple-Valued Logic and Soft Computing, 17 (2-3), pp. 183-206.
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