Learning Fuzzy Rule Based Classifier with Rule Weights Optimization and Structure Selection by a Genetic Algorithm

Author

Evsukoff, Alexandre G.

Author_Institution

Univ. Fed. do Rio de Janeiro, Rio de Janeiro

fYear

2007

fDate

23-26 July 2007

Firstpage

1

Lastpage

6

Abstract

This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimized in a structure selection search, performed by a genetic algorithm The method is tested against benchmark classification problems found in the literature, with good results.

Keywords

computational complexity; computational linguistics; fuzzy set theory; genetic algorithms; learning (artificial intelligence); least squares approximations; pattern recognition; benchmark classification problems; genetic algorithm; learning fuzzy rule; least squares sense; linguistic rules; model complexity; pattern recognition; rule weights optimization; structure selection; structure selection search; Association rules; Benchmark testing; Data mining; Design optimization; Fuzzy sets; Fuzzy systems; Genetic algorithms; Input variables; Machine learning; Parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International

Conference_Location

London

ISSN

1098-7584

Print_ISBN

1-4244-1209-9

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2007.4295471

Filename

4295471