@inproceedings{2a114af2cf6e492883d25eb7352637d0,
title = "Ensemble pruning using reinforcement learning",
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.",
author = "Ioannis Partalas and Grigorios Tsoumakas and Ioannis Katakis and Ioannis Vlahavas",
year = "2006",
month = jan,
day = "1",
doi = "10.1007/11752912_31",
language = "English",
isbn = "354034117X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "301--310",
booktitle = "Advances in Artificial Intelligence - 4th Helenic Conference on AI, SETN 2006, Proceedings",
note = "4th Helenic Conference on AI, SETN 2006 ; Conference date: 18-05-2006 Through 20-05-2006",
}