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Journal of Information Science and Engineering, Vol. 22 No. 1, pp. 175-188 (January 2006)

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

Shyi-Ming Chen and Hao-Lin Lin
Department of Computer Science and Information Engineering
+Department of Electronic Engineering
National Taiwan University of science and Technology
Taipei, 106 Taiwan

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.

Keywords: fuzzy rules, genetic algorithms, Iris data, weighted fuzzy rules, average classification accuracy rate

Full Text () Retrieve PDF document (200601_10.pdf)

Received January 14, 2004; revised June 9 & August 23, 2004; accepted August 31, 2004.
Communicated by Chin-Teng Lin.
* This work was supported in part by the National Science Council, R.O.C., under grant No. NSC 91-2213-E-011-037.