Abstract
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.
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