DocumentCode
10505
Title
Fuzzy Classifiers Tuning Through an Adaptive Memetic Algorithm
Author
Murcia, Cristhian ; Bonilla, G. ; Melgarejo, Miguel
Author_Institution
Univ. Distrital Francisco Jose de Caldas, Bogota, Colombia
Volume
12
Issue
2
fYear
2014
fDate
Mar-14
Firstpage
197
Lastpage
204
Abstract
This paper presents a methodological approach for tuning the fuzzy rules of a fuzzy classifier using an adaptive memetic algorithm. The approach is validated over two benchmark problems in terms of classification error and computational effort. In addition, it compares the performance of memetic, genetic and adaptive memetic algorithms over the benchmark problems. These results show a favorable trend towards the tuning of the classifiers through the adaptive memetic perspective.
Keywords
fuzzy set theory; genetic algorithms; pattern classification; adaptive memetic algorithm; adaptive memetic perspective; classification error; computational effort; fuzzy classifiers tuning; genetic algorithm; methodological approach; Benchmark testing; Breast cancer; Classification algorithms; Fuzzy logic; Media; Memetics; Tuning; Adaptative; Breast Cancer; Classifiers; Fuzzy Systems; Hyiperheuristic; Memetic Algorithm; Wine; local improvement; tuning;
fLanguage
English
Journal_Title
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher
ieee
ISSN
1548-0992
Type
jour
DOI
10.1109/TLA.2014.6749538
Filename
6749538
Link To Document