Self-optimising CBR retrieval.

Jarmulak, J.; Craw, S.; Rowe, R.

Abstract

One reason why Case-Based Reasoning (CBR) has become popular is because it reduces development cost compared to rule-based expert systems. Still, the knowledge engineering effort may be demanding. In this paper we present a tool which helps to reduce the knowledge acquisition effort for building a typical CBR retrieval stage consisting of a decision-tree index and similarity measure. We use genetic algorithms to determine the relevance/importance of case features and to find optimal retrieval parameters. The optimisation is done using the data contained in the case-base. Because no (or little) other knowledge is needed this results in a self-optimising CBR retrieval. To illustrate this we present how the tool has been applied to optimise retrieval for a tablet formulation problem.

Citation

JARMULAK, J., CRAW, S. and ROWE, R. 2000. Self-optimising CBR retrieval. In Proceedings of the 12th IEEE international conference on tools with artificial intelligence (ICTAI 2000), 13-15 November 2000, Vancouver, Canada. New York: IEEE [online], article number 889897, pages 376-383. Available from: https://doi.org/10.1109/TAI.2000.889897

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 12th IEEE international conference on tools with artificial intelligence (ICTAI 2000)
• Start Date: Nov 13, 2000
• End Date: Nov 15, 2000
• Acceptance Date: Nov 13, 2000
• Online Publication Date: Aug 6, 2002
• Publication Date: Dec 31, 2000
• Deposit Date: May 7, 2007
• Publicly Available Date: May 7, 2007
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 889897
• Pages: 376-383
• Series Title: Proceedings of the IEEE international conference on tools with artificial intelligence
• ISBN: 9780769509099
• DOI: https://doi.org/10.1109/TAI.2000.889897
• Keywords: Case based reasoning
• Public URL: http://hdl.handle.net/10059/64
• Contract Date: May 7, 2007