In this paper we present a distributed system in which several case-based reasoning (CBR) agents cooperate under a boosting schema. Each CBR agent knows part of the cases (a subset of the available attributes) and is trained with a subset of the available cases (so not all the agents know the same cases). The solution of the system is then computed by means of a weighted average of the solutions provided by the CBR agents. Weights are actively learnt by a genetic algorithm. The system has been applied to a breast cancer application domain. The results show that with our methodology we can improve the results obtained with a case base in which attributes have been manually selected by physicians, saving physicians work in future. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
López, B., Pous, C., Pla, A., & Gay, P. (2009). Boosting CBR agents with genetic algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5650 LNAI, pp. 195–209). https://doi.org/10.1007/978-3-642-02998-1_15
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