Abstract
There is an increasing interest on ensemble learning since it reduces the bias-variance problem of several classifiers. In this paper we approach an ensemble learning method in a multi-agent environment. Particularly, we use genetic algorithms to learnt weights in a boosting scenario where several case-based reasoning agents cooperate. In order to deal with the genetic algorithm results, we propose several multi-criteria decision making methods. We experimentally test the methods proposed in a breast cancer diagnosis database.
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López, B., Pous, C., Gay, P., Pla, A. (2009). Multi Criteria Decision Methods for Coordinating Case-Based Agents. In: Braubach, L., van der Hoek, W., Petta, P., Pokahr, A. (eds) Multiagent System Technologies. MATES 2009. Lecture Notes in Computer Science(), vol 5774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04143-3_6
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DOI: https://doi.org/10.1007/978-3-642-04143-3_6
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