A new method to deal with fuzzy classification problems by tuning membership functions for fuzzy classification systems

Abstract

This paper presents a new method to construct and tune membership functions and generate fuzzy classification rules from training instances for handling the Iris data classification problem. First, we find two attributes of the Iris data from the training instances that are suitable to serve as classification criteria. Then, we construct and tune the membership functions of these two attributes and generate fuzzy classification rules from the training instances. The proposed method generates the same number of fuzzy classification rules as the number of species of the training instances. It generates fewer fuzzy classification rules and can get a higher average classification accuracy rate than the existing methods.