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Title: Ensemble Pruning using Reinforcement Learning
Author(s): I. Partalas, G. Tsoumakas, I. Katakis, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (10 pages).
Keywords: Reinforcement Learning, Multiple Classifier Systems, Ensemble Prunning, Classification.
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.
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.
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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.
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
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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