Session:

Hybrid System Applications
Sunday, February 29, 2004, 17.15 – 17.35

Session Chair:

Fikret Gürgen

Paper Title:

Discovering Fuzzy Classifiers by Genetic Algorithm

Author(s):

M. Hasanzade, Sharif University of Technology of Iran, Iran
Prof. S. Bagheri, Sharif University of Technology of Iran, Iran
Prof. C. Lucas, Tehran University of Iran, Iran

Abstract:

Today’s solving most of application problems results in solving a data classification problem. Lots of solutions are proposed for classification problems. Most of them concentrate on reducing detection error of classifiers. For error reduction fuzzy logic can be useful. Obtaining error free and optimized classifiers could be done by evolutionary algorithms. Base on these, we proposed a machine learning based method for discovering fuzzy classifiers (a set of fuzzy rules) by genetic algorithms. The proposed method is tested by a number of benchmark data sets. Results in these tests are better than those of similar systems. This paper exhibits the obtained results.