Automated knowledge acquisition for a fuzzy classification problem

Author

Whitfort, Tim ; Matthews, Chris ; Jagielska, Ilona

Author_Institution

Dept. of Inf. Technol., La Trobe Univ., Bundoora, Vic., Australia

fYear

1995

Firstpage

227

Lastpage

230

Abstract

Genetic algorithms and neural networks are useful as automated knowledge acquisition tools for Fuzzy Systems. This paper describes the application of these techniques to a well known classification problem, namely the iris species classification problem. The performance of the resulting fuzzy systems exceed that reported for those derived using alternative methods. Preliminary work indicates that the use of genetic algorithms is the more flexible as it allows the simultaneous acquisition of fuzzy set parameters and fuzzy rules

Keywords

fuzzy neural nets; genetic algorithms; knowledge acquisition; learning by example; pattern classification; Fuzzy Systems; automated knowledge acquisition; expert systems; fuzzy classification problem; fuzzy rule base; fuzzy rules; fuzzy set parameters; genetic algorithms; iris species classification problem; neural networks; Data mining; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Hybrid intelligent systems; Information technology; Iris; Knowledge acquisition; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on

Conference_Location

Dunedin

Print_ISBN

0-8186-7174-2

Type

conf

DOI

10.1109/ANNES.1995.499477

Filename

499477