Soft Computing

, Volume 13, Issue 5, pp 511–519 | Cite as

Parallel distributed genetic fuzzy rule selection

Focus

Abstract

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.

Keywords

Genetic fuzzy rule selection Parallel distributed implementation Data subdivision Fuzzy rule-based classifier 

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Graduate School of EngineeringOsaka Prefecture UniversitySakaiJapan

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