A knowledge acquisition method for fuzzy expert systems in diagnosis problems

Author

Evsukoff, Alexandre ; Gentil, Sylviane ; Branco, Antonio C S

Author_Institution

Lab. d´´Autom. de Grenoble, CNRS, Grenoble, France

Volume

3

fYear

1997

fDate

1-5 Jul 1997

Firstpage

1411

Abstract

In this paper a method for knowledge acquisition in diagnosis problems is presented. This method results in a zero-order Sugeno rule base where the combinatorial explosion of rules is solved by a decomposition scheme. This approach allows a unified representation, where the knowledge obtained from data by a supervised learning algorithm can be directly confronted with the knowledge elicited from the experts. The supervised learning algorithm is rested upon some classification problems found in literature

Keywords

diagnostic expert systems; fuzzy set theory; fuzzy systems; knowledge acquisition; knowledge representation; learning (artificial intelligence); pattern classification; decomposition; expert diagnostic systems; fuzzy expert systems; knowledge acquisition; knowledge elicitation; pattern classification; supervised learning; zero-order Sugeno rule base; Diagnostic expert systems; Explosions; Fuzzy sets; Hybrid intelligent systems; Knowledge acquisition; Pattern recognition; Supervised learning; Testing; Uncertainty; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on

Conference_Location

Barcelona

Print_ISBN

0-7803-3796-4

Type

conf

DOI

10.1109/FUZZY.1997.619750

Filename

619750