using distribution of data to enhance performance of fuzzy classification systems

Authors

eghbal g. mansoori

mansoor j. zolghadri

seraj d. katebi

abstract

this paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. the classification performance andinterpretability are of major importance in these systems. in this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). ourapproach uses a punishment algorithm to reduce the decision subspace of a ruleby reducing its weight, such that its performance is enhanced. obviously, thisreduction will cause the decision subspace of adjacent overlapping rules to beincreased and consequently rewarding these rules. the results of computersimulations on some well-known data sets show the effectiveness of ourapproach.

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Journal title:
iranian journal of fuzzy systems

Publisher: university of sistan and baluchestan

ISSN 1735-0654

volume 4

issue 1 2007

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