A Hybrid CBR Model for Forecasting in Complex Domains
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
An Automated Hybrid CBR System for Forecasting
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Neuro-symbolic System for Forecasting Red Tides
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Retrieval strategies for case-based reasoning: a categorised bibliography
The Knowledge Engineering Review
A new CBR approach to the oil spill problem
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Case-based polishing process planning with Fuzzy Set Theory
Journal of Intelligent Manufacturing
Contract net protocol using fuzzy case based reasoning
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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Both information retrieval and case-based reasoning systems rely on effective and efficient selection of relevant data. Typically, relevance in such systems is approximated by similarity or indexing models. However, the definition of what makes data items similar or how they should be indexed is often nontrivial and time-consuming. Based on growing cell structure artificial neural networks, this paper presents a method that automatically constructs a case retrieval model from existing data. Within the case-based reasoning (CBR) framework, the method is evaluated for two medical prognosis tasks, namely, colorectal cancer survival and coronary heart disease risk prognosis. The results of the experiments suggest that the proposed method is effective and robust. To gain a deeper insight and understanding of the underlying mechanisms of the proposed model, a detailed empirical analysis of the models structural and behavioral properties is also provided