New Methods for Handling Fuzzy Classification Problems for Fuzzy Classification Systems

• Main Authors: Cheng-Hao Yu, 游承澔
• Other Authors: Shyi-Ming Chen
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/38491461139349654318

碩士 === 國立臺灣科技大學 === 電子工程系 === 90 === The fuzzy classification system is an important application of the fuzzy set theory. Fuzzy classification systems can deal with perceptual uncertainties in classification problems. In this thesis, we proposed two methods to deal with fuzzy classification problems for fuzzy classification systems. The first method can deal with fuzzy classification problems based on the concept of fuzzy compatibility relations for finding the cluster centers of training instances. The proposed method can get a higher average classification accuracy rate than the existing methods. The second method is based on the exclusion of useless input attributes to generate fuzzy rules from training instances to deal with the Iris data classification problem. It can discard some useless input attributes to improve the average classification accuracy rate. The proposed method can get a higher average classification accuracy rate and can generate fewer fuzzy rules and fewer inputs fuzzy sets in the generated fuzzy rules than the existing methods.