Effect of rule weights in fuzzy rule-based classification systems

Abstract

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy rule that has the maximum compatibility grade with the new pattern. When we use fuzzy rules with certainly grades, the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy rules with/without certainty grades. It is also shown that certainly grades play an important role when a fuzzy rule-based classification system is a mixture of general rules and specific rules. Through computer simulations, we show that fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy rules with certainty grades.