Generating fuzzy rules from training instances for fuzzy classification systems |
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| Authors: | Shyi-Ming Chen Fu-Ming Tsai |
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| Affiliation: | aDepartment of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC;bDepartment of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC;cDepartment of Computer Science and Information Engineering, Jinwen University of Science and Technology, Taipei County, Taiwan, ROC |
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| Abstract: | In recent years, many methods have been proposed to generate fuzzy rules from training instances for handling the Iris data classification problem. In this paper, we present a new method to generate fuzzy rules from training instances for dealing with the Iris data classification problem based on the attribute threshold value α, the classification threshold value β and the level threshold value γ, where α  [0, 1], β [0, 1] and γ [0, 1]. The proposed method gets a higher average classification accuracy rate than the existing methods. |
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| Keywords: | Fuzzy rules Fuzzy sets Fuzzy classification systems Iris data Membership functions |
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