USING DISTRIBUTION OF DATA TO ENHANCE PERFORMANCE OF FUZZY CLASSIFICATION SYSTEMS

Document Type: Research Paper

Authors

COMPUTER SCIENCE AND ENGINEERING DEPARTMENT, COLLEGE OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN

Abstract

This paper considers the automatic design of fuzzy rule-based
classification systems based on labeled data. The classification performance and
interpretability are of major importance in these systems. In this paper, we
utilize the distribution of training patterns in decision subspace of each fuzzy
rule to improve its initially assigned certainty grade (i.e. rule weight). Our
approach uses a punishment algorithm to reduce the decision subspace of a rule
by reducing its weight, such that its performance is enhanced. Obviously, this
reduction will cause the decision subspace of adjacent overlapping rules to be
increased and consequently rewarding these rules. The results of computer
simulations on some well-known data sets show the effectiveness of our
approach.

Keywords


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