A New Method to Generate Fuzzy Rules from Numerical Data based on the Exclusion of Attribute Terms

Abstract

To develop a fuzzy system, the most important task is to derive a set of fuzzy rules from a set of training data. In recent years, many methods have been developed to automatically derive fuzzy rules from training instances. In this paper,we present a new method to generate fuzzy rules from numerical data based on the exclusion of attribute terms to deal with the Iris data classification problem. The experimental results show that the proposed method can get a higher classification accuracy rate than the existing methods.