Improving the performance of fuzzy classifier systems for patternclassification problems with continuous attributes
Ishibuchi, H.; Nakaskima, T.
Industrial Electronics, IEEE Transactions on
Volume 46, Issue 6, Dec 1999 Page(s):1057 - 1068
Digital Object Identifier 10.1109/41.807986
Summary:In this paper, various methods are introduced for improving the
ability of fuzzy classifier systems to automatically generate fuzzy
if-then rules for pattern classification problems with continuous
attributes. First, we describe a simple fuzzy classifier system where a
randomly generated initial population of fuzzy if-then rules is evolved
by typical genetic operations, such as selection, crossover, and
mutation. By computer simulations on a real-world pattern classification
problem with many continuous attributes, we show that the search ability
of such a simple fuzzy classifier system is not high. Next, we examine
the search ability of a hybrid algorithm where a learning procedure of
fuzzy if-then rules is combined with the fuzzy classifier system. Then,
we introduce two heuristic procedures for improving the performance of
the fuzzy classifier system. One is a heuristic rule generation
procedure for an initial population where initial fuzzy if-then rules
are directly generated from training patterns. The other is a heuristic
population update procedure where new fuzzy if-then rules are generated
from misclassified and rejected training patterns, as well as from
existing fuzzy if-then rules by genetic operations. By computer
simulations, we demonstrate that these two heuristic procedures
drastically improve the search ability of the fuzzy classifier system.
We also examine a variant of the fuzzy classifier system where the
population size (i.e., the number of fuzzy if-then rules) varies
depending on the classification performance of fuzzy if-then rules in
the current population
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