PROCEEDINGS IPMU '08
Computational Efficiency of Parallel Distributed Genetic Fuzzy Rule Selection for Large Data Sets
Yusuke Nojima, Hisao Ishibuchi.
Genetic fuzzy rule selection is a
two-phase classification rule mining
method. First a large number of
candidate fuzzy rules are generated
by an association rule mining technique.
Then only a small number
of generated rules are selected
by a genetic algorithm. We have
already proposed an idea of parallel
distributed implementation of genetic
fuzzy rule selection. In this paper,
we examine its computational
efficiency for large data sets through
computational experiments using a
cluster system.
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