Automated knowledge acquisition for a fuzzy classification problem

Dunedin, New Zealand November 20-November 23

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http://doi.ieeecomputersociety.org/10.1109/ANNES.1995.499477

2nd New Zealand Two-Stream Internatio ...

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	T. Whitfort, C. Matthews, I. Jagielska, "Automated knowledge acquisition for a fuzzy classification problem," Artificial Neural Networks and Expert Systems, New Zealand Conference, pp. 227, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995.

	BibTex	x
	@article{ 10.1109/ANNES.1995.499477, author = {T. Whitfort and C. Matthews and I. Jagielska}, title = {Automated knowledge acquisition for a fuzzy classification problem}, journal ={Artificial Neural Networks and Expert Systems, New Zealand Conference}, volume = {0}, year = {1995}, isbn = {0-8186-7174-2}, pages = {227}, doi = {http://doi.ieeecomputersociety.org/10.1109/ANNES.1995.499477}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Artificial Neural Networks and Expert Systems, New Zealand Conference TI - Automated knowledge acquisition for a fuzzy classification problem SN - 0-8186-7174-2 SP EP A1 - T. Whitfort, A1 - C. Matthews, A1 - I. Jagielska, PY - 1995 KW - knowledge acquisition; pattern classification; fuzzy neural nets; genetic algorithms; learning by example; automated knowledge acquisition; fuzzy classification problem; genetic algorithms; neural networks; Fuzzy Systems; iris species classification problem; fuzzy set parameters; fuzzy rules; fuzzy rule base; expert systems VL - 0 JA - Artificial Neural Networks and Expert Systems, New Zealand Conference ER -

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

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

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

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.

Index Terms:

knowledge acquisition; pattern classification; fuzzy neural nets; genetic algorithms; learning by example; automated knowledge acquisition; fuzzy classification problem; genetic algorithms; neural networks; Fuzzy Systems; iris species classification problem; fuzzy set parameters; fuzzy rules; fuzzy rule base; expert systems

Citation:

T. Whitfort, C. Matthews, I. Jagielska, "Automated knowledge acquisition for a fuzzy classification problem," annes, pp.227, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995

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