Self-optimising CBR retrieval

Author

Jarmulak, Jacek ; Craw, Susan ; Rowe, Ray

Author_Institution

Sch. of Comput. & Math. Sci., Robert Gordon´´s Univ., UK

fYear

2000

fDate

2000

Firstpage

376

Lastpage

383

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

Keywords

case-based reasoning; genetic algorithms; information retrieval; knowledge acquisition; decision-tree index; genetic algorithms; knowledge acquisition; knowledge engineering; optimal retrieval parameters; self-optimising case based reasoning retrieval; similarity measure; tablet formulation problem; Artificial intelligence; Costs; Drugs; Expert systems; Indexing; Information retrieval; Knowledge acquisition; Knowledge engineering; Powders; Problem-solving;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on

Conference_Location

Vancouver, BC

ISSN

1082-3409

Print_ISBN

0-7695-0909-6

Type

conf

DOI

10.1109/TAI.2000.889897

Filename

889897