A genetic algorithm for tuning fuzzy rule-based classification systems with Interval-Valued Fuzzy Sets

Author

Sanz, J. ; Fernández, A. ; Bustince, H. ; Herrera, F.

Author_Institution

Dept. of Automatics & Comput., Univ. of Navarre, Pamplona, Spain

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

3

Abstract

Fuzzy Rule-Based Classification Systems are a widely used tool in Data Mining because of the interpretability given by the concept of linguistic label. However, the use of this type of models implies a degree of uncertainty in the definition of the fuzzy partitions. In this work we will use the concept of Interval-Valued Fuzzy Set to deal with this problem. The aim of this contribution is to show the improvement in the performance of linguistic Fuzzy Rule-Based Classification Systems afterward the application of a cooperative tuning methodology between the tuning of the amplitude of the support and the lateral tuning (based on the 2-tuples fuzzy linguistic model) applied to the linguistic labels modeled with Interval-Valued Fuzzy Sets.

Keywords

data mining; fuzzy set theory; genetic algorithms; 2-tuples fuzzy linguistic model; cooperative tuning methodology; data mining; fuzzy partition; genetic algorithm; interval-valued fuzzy set; lateral tuning; linguistic fuzzy rule-based classification; Biological cells; Computational modeling; Fuzzy sets; Genetics; Pragmatics; Tuning; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ), 2010 IEEE International Conference on

Conference_Location

Barcelona

ISSN

1098-7584

Print_ISBN

978-1-4244-6919-2

Type

conf

DOI

10.1109/FUZZY.2010.5584097

Filename

5584097