Self-optimising CBR retrieval.
Jarmulak, J.; Craw, S.; Rowe, R.
Professor Susan Craw email@example.com
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.
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||IEEE Institute of Electrical and Electronics Engineers|
|Series Title||Proceedings of the IEEE international conference on tools with artificial intelligence|
|Keywords||Case based reasoning|
JARMULAK 2000 Self-optimising CBR retrieval
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