Generating Weighted Fuzzy Rules from Training Instances Using Genetic Algorithms to Handle the Iris Data Classification Problem

In recent years, many researchers have focused on applying the fuzzy set theory to generate fuzzy rules from training instances to deal with the Iris data classification problem. In this paper, we propose a new method to automatically generate weighted fuzzy rules from training instances by using genetic algorithms to handle the Iris data classification problem, where the attributes appearing in the antecedent parts of the generated fuzzy rules have different weights. The proposed method can achieve a higher average classification accuracy rate and generate fewer fuzzy rules than the existing methods.