Self-optimising CBR retrieval

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Venue:
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Year:
2000

Citing 0
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Abstract

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.