Ensemble Pruning using Reinforcement Learning

I. Partalas, G. Tsoumakas, I. Katakis, I. Vlahavas (2006) “Ensemble Pruning using Reinforcement Learning”, 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.

Reinforcement Learning, Multiple Classifier Systems, Ensemble Prunning, Classification.

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.