Title :
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
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;
Conference_Titel :
Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7803-3796-4
DOI :
10.1109/FUZZY.1997.619750
