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
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ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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An Assessment of Case-Based Reasoning for Spam Filtering
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Trust-based evolutionary game model assisting AODV routing against selfishness
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Modeling Reuse on Case-Based Reasoning with Application to Breast Cancer Diagnosis
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ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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CIS'04 Proceedings of the First international conference on Computational and Information Science
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IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
eXiT*CBR: A framework for case-based medical diagnosis development and experimentation
Artificial Intelligence in Medicine
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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.