GENERATING FUZZY RULES FROM TRAINING DATA CONTAINING NOISE FOR HANDLING CLASSIFICATION PROBLEMS |
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| Authors: | SHYI-MING CHEN CHENG-HSUAN KAO CHENG-HAO YU |
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| Affiliation: | 1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.;2. Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C. |
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| Abstract: | It is obvious that one of the important tasks in a fuzzy system is to find a set of rules to deal with a specific classification problem. In recent years, many researchers focused on the research topic of generating fuzzy rules from training data for handling classification problems. In a previous paper, we presented an algorithm to construct membership functions and to generate fuzzy rules from training examples. In this paper, we extend that work to propose a new algorithm to generate fuzzy rules from training data containing noise to deal with classification problems. The proposed algorithm gets a higher classification accuracy rate and generates fewer fuzzy rules and fewer input attributes in the antecedent portions of the generated fuzzy rules. |
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