Boosting CBR Agents with Genetic Algorithms
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.
KeywordsDistributed CBR genetic algorithms boosting multi-agent systems
Unable to display preview. Download preview PDF.
- 3.Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation 4(4) (1996)Google Scholar
- 15.Nagendra-Prasad, M.V., Plaza, E.: Corporate memories as distributed case libraries. In: 10th Banff Knowledge Acquisition for Knowledge-based Systems Workshop, pp. 1–19 (1996)Google Scholar
- 16.Ontañón, S., Plaza, E.: A bartering approach to improve multiagent learning. In: Int. Conf. Autonomous Agents and Multiagent Systems (AAMAS), pp. 386–393 (2002)Google Scholar
- 18.Pohlheim, H.: Genetic and evolutionary algorithm toolbox for use with matlab (1994), http://www.geatbx.com/docu/index.html
- 20.Pous, C., Gay, P., Pla, A., López, B.: Collecting methods for medical CBR development and experimentation. In: Schaaf, M. (ed.) Workshop Proceedings of the 9th European Conference on Case-Based Reasoning, CBR in the Health Sciences (ECCBR-HC), Trier, pp. 89–98. Tharax-Verlag (2008)Google Scholar
- 25.Teodorescu, E.I., Petridis, M.: An architecture for multiple heterogeneous case-based reasoning employing agent technologies. In: CIMAS (2008), http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/
- 28.Yalabik, I., Yarman-Vural, F.T., Uçoluk, G., Sehitoglu, O.T.: A pattern classification approach for boosting with genetic algorithms. In: 22nd International Symposium on Computer and Information Sciences, pp. 1–6 (2007)Google Scholar