Parallel distributed implementation of genetics-based machine learning for fuzzy classifier design

Authors:
Yusuke Nojima;Shingo Mihara;Hisao Ishibuchi
Affiliations:
Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan;Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan;Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

Evolutionary algorithms have been successfully applied to design fuzzy rule-based classifiers. They are used for attribute selection, fuzzy set selection, rule selection, membership function tuning, and so on. Genetics-based machine learning (GBML) is one of the promising evolutionary algorithms for classifier design. It can find an appropriate combination of antecedent sets for each rule in a classifier. Although GBML has high search ability, it needs long computation time especially for large data sets. In this paper, we apply a parallel distributed implementation to our fuzzy genetics-based machine learning. In our method, we divide not only a population but also a training data set into subgroups. These subgroups are assigned to CPU cores. Through computational experiments on large data sets, we show the effectiveness of the proposed parallel distributed implementation.