A fuzzy associative classification system with genetic rule selection for high-dimensional problems

Author

Alcalá-Fdéz, J. ; Alcalá, R. ; Herrera, F.

Author_Institution

Dept. of Comput. Sci. & Artificial Intell., Univ. of Granada, Granada, Spain

fYear

2010

fDate

17-19 March 2010

Firstpage

33

Lastpage

38

Abstract

The learning of Fuzzy Rule-Based Classification Systems for High-Dimensional problems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. In this work, we propose a fuzzy association rule-based classification method with genetic rule selection for high-dimensional problems to obtain an accurate and compact fuzzy rule-based classifier with low computational cost. The results obtained from the comparison with other two genetic fuzzy systems over nine real-world datasets with different characteristics show the effectiveness of the proposed approach.

Keywords

data mining; fuzzy set theory; genetic algorithms; pattern classification; fuzzy associative classification system; fuzzy rule based classification system; fuzzy rule search space; genetic fuzzy system; genetic rule selection; high dimensional problem; low computational cost; Association rules; Classification tree analysis; Computational efficiency; Computer science; Data mining; Databases; Fuzzy sets; Fuzzy systems; Genetics; Scalability;

fLanguage

English

Publisher

ieee

Conference_Titel

Genetic and Evolutionary Fuzzy Systems (GEFS), 2010 4th International Workshop on

Conference_Location

Mieres

Print_ISBN

978-1-4244-4621-6

Electronic_ISBN

978-1-4244-4622-3

Type

conf

DOI

10.1109/GEFS.2010.5454160

Filename

5454160