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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cie.2005.06.007 Byline: Nai Ren Guo, Tzuu-Hseng S. Li, Chao-Lin Kuo Abstract: This paper proposes a new hierarchical fuzzy model (HFM) to solve the classification problem. The developed classification model comprises of two stages; one is to generate the fuzzy IF-THEN rules for each subsystem and the other is to determine the classification unit. For the classification problem, number of rules and the correct classification rate are the fundamental requirements. In this paper, we also advance two genetic algorithms (GAs) to tune the HFM. One is used to determine the combination of the input features for each subsystem on the HFM and the other is to reduce the number of rules in each fuzzy subsystem. The performance has been tested by simulations on the well known Wine and Iris databases. Simulations demonstrate that the proposed HFM under a few rules can provide sufficiently high classification rate even with higher feature dimensions. Author Affiliation: IC.sup.2S Laboratory, Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan, ROC Article History: Received 24 June 2003; Revised 9 January 2005; Accepted 1 June 2005