Voting in fuzzy rule-based systems for pattern classification problems

Abstract

In this paper, we examine two kinds of voting schemes in fuzzy rule-based systems for pattern classification problems. One is the voting by multiple fuzzy if-then rules in a single fuzzy rule-based classification system. The other is the voting by multiple fuzzy rule-based classification systems. First, we discuss the voting by multiple fuzzy if-then rules, which is used as a fuzzy reasoning method for classifying input patterns in a single fuzzy rule-based classification system. The performance of the voting by multiple fuzzy if-then rules is examined by computer simulations on the iris data. Next, we discuss the voting by multiple fuzzy rule-based classification systems. Three voting methods (i.e., a perfect unison rule, a majority rule, and a weighted voting rule) are used for combining classification results by multiple fuzzy rule-based classification systems. Finally, we compare the performance of fuzzy rule-based classification systems with that of other classification methods such as neural networks and statistical techniques by computer simulations on some well-known test problems.