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 effortmay 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 casebase. 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.
|Start Date||Nov 13, 2000|
|Publication Date||Dec 31, 2000|
|Publisher||Institute of Electrical and Electronics Engineers|
|Series Title||Proceedings of the IEEE international conference on tools with artificial intelligence|
|Institution 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|
|Keywords||Case based reasoning|
JARMULAK 2000 Self-optimising CBR retrieval
You might also like
Representing temporal dependencies in human activity recognition.
Fall prediction using behavioural modelling from sensor data in smart homes.
Monitoring health in smart homes using simple sensors.
An e-learning recommender that helps learners find the right materials.