Data set subdivision for parallel distributed implementation of genetic fuzzy rule selection

被引：0
|
作者：
Nojima, Yusuke [1 ]
Kuwajima, Isao [1 ]
Ishibuchi, Hisao [1 ]
机构：
[1] Osaka Prefecture Univ, Grad Sch Engn, Dept Comp Sci & Intelligent Syst, Naka Ku, Osaka 5998531, Japan
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4 | 2007年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Genetic fuzzy rule selection has been successfully used to design accurate and interpretable fuzzy classifiers. However there exists a computational complexity problem for large data sets. This paper proposes a simple but effective idea to improve the applicability of genetic fuzzy rule selection to large data sets. Our idea is based on the parallel distributed implementation of genetic fuzzy rule selection. We examine the advantage of the proposed approach through computational experiments on some benchmark data sets.
引用
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页码：2011 / 2016
页数：6
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