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Abstract
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.
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Authors and Affiliations
Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW, 2007, Australia
Ning Lu, Jie Lu & Guangquan Zhang
- Ning Lu
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- Jie Lu
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- Guangquan Zhang
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Editors and Affiliations
University of Chile, Republica 701, 8370439, Santiago, Chile
Juan D. Velásquez & Sebastián A. Ríos &
University of Brighton, BN2 4GJ, Brighton, UK
Robert J. Howlett
University of South Australia, 5095, Mawson Lakes, SA, Australia
Lakhmi C. Jain
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Lu, N., Lu, J., Zhang, G. (2009). An Integrated Knowledge Adaption Framework for Case-Based Reasoning Systems. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_47
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