Parallel distributed genetic fuzzy rule selection

  • Authors:

  • Yusuke Nojima;Hisao Ishibuchi;Isao Kuwajima

  • Affiliations:

  • Osaka Prefecture University, Graduate School of Engineering, Sakai, Osaka, Japan;Osaka Prefecture University, Graduate School of Engineering, Sakai, Osaka, Japan;Osaka Prefecture University, Graduate School of Engineering, Sakai, Osaka, Japan

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
  • Year:
  • 2008

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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.