Abstract
This paper proposes an ensemble learning method of fuzzy rule-based classification systems. Two different types of fuzzy rule-based systems are involved in our ensemble learning method. One is a fuzzy classification system whose output is the suggested class of an input pattern. The other type of fuzzy rule-based system assigns a credit to each fuzzy classification system. A boosting method is used to construct a collection of fuzzy classification systems. In the boosting method, a weight is assigned to each training pattern. Training patterns with a large weight are used to construct a fuzzy classification system. We show the effectiveness of the proposed method in computer simulations.
