Title :
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
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;
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
DOI :
10.1109/GEFS.2010.5454160
