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Erschienen in:
2009 | OriginalPaper | Buchkapitel
An Integrated Knowledge Adaption Framework for Case-Based Reasoning Systems
verfasst von : Ning Lu, Jie Lu, Guangquan Zhang
Erschienen in: Knowledge-Based and Intelligent Information and Engineering Systems
Verlag: Springer Berlin Heidelberg
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The development of effective knowledge adaption techniques is one of the promising solutions to improve the performance of case-based reasoning (CBR) systems. Case-base maintenance becomes a powerful method to refine knowledge in CBR systems. This paper proposes an integrated knowledge adaption framework for CBR systems which contains a meta database component and a maintenance strategies component. The meta database component can help track changes of interested concepts and therefore enable a CBR system to signal a need for maintenance or to invoke adaption on its own. The maintenance strategies component can perform cross-container maintenance operations in a CBR system. This paper also illustrates how the proposed integrated knowledge adaption framework assists decision makers to build dynamic prediction and decision capabilities.