A fast genetic method for inducting linguistically understandable fuzzy models

Author

Sánchez, Luciano

Author_Institution

Depto. Inf., Univ. de Oviedo, Gijon, Spain

Volume

3

fYear

2001

fDate

25-28 July 2001

Firstpage

1559

Abstract

Fuzzy rule bases can be regarded as mixtures of experts, and boosting techniques can be applied to learn them from data. In particular, provided that adequate reasoning methods are used, fuzzy models are extended additive models, thus backfitting can be applied to them. We propose to use an implementation of backfitting that uses a genetic algorithm for fitting submodels to residuals and we also show that it is both more accurate and faster than other fuzzy rule learning methods

Keywords

computational linguistics; fuzzy logic; fuzzy set theory; genetic algorithms; inference mechanisms; learning (artificial intelligence); uncertainty handling; backfitting; boosting techniques; extended additive models; fast genetic method; fuzzy models; fuzzy rule bases; fuzzy rule learning methods; genetic algorithm; linguistically understandable fuzzy model induction; mixtures of experts; reasoning methods; residuals; submodel fitting; Artificial intelligence; Fuzzy reasoning; Fuzzy sets; Genetic algorithms; Learning systems; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.943781

Filename

943781