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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