A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Design and application of hybrid intelligent systems
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Trust-based evolutionary game model assisting AODV routing against selfishness
Journal of Network and Computer Applications
Pruning adaptive boosting ensembles by means of a genetic algorithm
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis
Decision Support Systems
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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 multicriteria decision making methods. We experimentally test the methods proposed in a breast cancer diagnosis database.