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Self-optimising CBR retrieval.

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

Authors

J. Jarmulak

R. Rowe



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

Conference Name 12th IEEE international conference on tools with artificial intelligence (ICTAI 2000)
Conference Location Vancouver, Canada
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)
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

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