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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Lu, N., Lu, J., & Zhang, G. (2009). An integrated knowledge adaption framework for case-based reasoning systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5712 LNAI, pp. 372–379). https://doi.org/10.1007/978-3-642-04592-9_47
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