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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Ensemble Pruning Using Reinforcement Learning
verfasst von : Ioannis Partalas, Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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.