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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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