Parallel distributed genetic fuzzy rule selection

Genetic fuzzy rule selection has been successfully used to design accurate and compact fuzzy rule-based classifiers. It is, however, very difficult to handle large data sets due to the increase in computational costs. This paper proposes a simple but effective idea to improve the scalability of gene...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 13; H. 5; S. 511 - 519
Hauptverfasser: Nojima, Yusuke, Ishibuchi, Hisao, Kuwajima, Isao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer-Verlag 01.03.2009
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Classification
Computational Intelligence
Computing costs
Control
Costs
Data mining
Datasets
Design
Engineering
Focus
Fuzzy sets
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Robotics
Subgroups
Fuzzy rule-based classifier
Parallel distributed implementation
Genetic fuzzy rule selection
Data subdivision
ISSN:1432-7643, 1433-7479
Online-Zugang:Volltext
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Zusammenfassung:Genetic fuzzy rule selection has been successfully used to design accurate and compact fuzzy rule-based classifiers. It is, however, very difficult to handle large data sets due to the increase in computational costs. This paper proposes a simple but effective idea to improve the scalability of genetic fuzzy rule selection to large data sets. Our idea is based on its parallel distributed implementation. Both a training data set and a population are divided into subgroups (i.e., into training data subsets and sub-populations, respectively) for the use of multiple processors. We compare seven variants of the parallel distributed implementation with the original non-parallel algorithm through computational experiments on some benchmark data sets.
Bibliographie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-008-0365-1