USING DISTRIBUTION OF DATA TO ENHANCE PERFORMANCE OF FUZZY CLASSIFICATION SYSTEMS

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.

Keywords:

Fuzzy rule-based classification systems , Rule weight