Parallel distributed genetic fuzzy rule selection
Parallel distributed genetic fuzzy rule selection
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DOI：
10.1007/s00500-008-0365-1
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发表时间：
2008-12
期刊：
影响因子：
4.1
通讯作者：
Y. Nojima;H. Ishibuchi;I. Kuwajima
中科院分区：
文献类型：
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作者：
Y. Nojima;H. Ishibuchi;I. Kuwajima
遗传模糊规则选择已被成功地用于设计准确和紧凑的模糊规则为基础的分类器。然而，由于计算成本的增加，很难处理大型数据集。本文提出了一种简单而有效的思想，以提高遗传模糊规则选择的大数据集的可扩展性。我们的想法是基于其并行分布式实现。训练数据集和群体都被分成子组（即，分别分成训练数据子集和子群体）以供多个处理器使用。我们比较了七个变种的并行分布式实现与原来的非并行算法，通过一些基准数据集上的计算实验。
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.