Acquisition of fuzzy rules using fuzzy ID3 with ability of learning for AND/OR operators

Author

Hayashi, Isao

Author_Institution

Dept. of Comput. & Ind. Sci., Hannan Univ., Osaka, Japan

fYear

1996

fDate

18-20 Nov 1996

Firstpage

187

Lastpage

190

Abstract

An ability of learning for AND/OR operators is discussed to overcome a drawback of fuzzy ID3. In the fuzzy ID3, it is nearly impossible to obtain the most suitable fuzzy rules since the fuzzy ID3 has a couple of problems, i.e., a problem of a lack of representation and an adjusting problem. In our fuzzy ID3, AND/OR operators are formulated using t-norm and t-conorm connectives with parameters and each parameter is adjusted using golden section method. By using golden section method, we get the optimal solution at a high speed. The proposed fuzzy ID3 gives more accurate fuzzy rules by adjusting parameters. If t-conorm connective is selected as AND/OR operator, the decision tree has more flexible representation

Keywords

fuzzy logic; fuzzy set theory; learning (artificial intelligence); neural nets; AND/OR operators; decision tree; fuzzy ID3; fuzzy rules; fuzzy rules acquisition; learning; t-conorm; t-norm; Arithmetic; Australia; Boundary conditions; Computer industry; Decision trees; Equations; Information systems; Intelligent systems; Mutual information; Production;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems, 1996., Australian and New Zealand Conference on

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-3667-4

Type

conf

DOI

10.1109/ANZIIS.1996.573929

Filename

573929