Modern Information Retrieval
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Credible Case-Based Inference Using Similarity Profiles
IEEE Transactions on Knowledge and Data Engineering
Dynamic refinement of feature weights using quantitative introspective learning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
CBR Supports Decision Analysis with Uncertainty
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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Credible case-based inference (CCBI) is a new and theoretically sound inferencing mechanism for case-based systems. In this paper, we formally investigate the level of precision that CCBI-based retrieval results may yield. Building upon our theoretical findings, we derive a number of optimization criteria that can be utilized for learning such similarity measures that bring about more precise predictions when used in the scope of CCBI. Our empirical experiments support the claim that, given appropriate similarity measures, CCBI can be enforced to produce highly precise predictions while its corresponding level of confidence is only marginally impaired.