00000cab a22000003a 4500 UP-99796217609517194 Buklod 20231007234352.0 m |o d | ta 101102s xx d | ||r ||||| || DENGII eng Tang-Kai Yin A Characteristic-Point-Based Fuzzy Inference Classifier by a Closeness Matrix. pp. 673-687 In this paper, a characteristic-point-based fuzzy inference classifier (CPFIC) is proposed to perform two-class classification. Through fuzzy interpolation, a subset of classified samples can be taken as representatives of all samples. They are called characteristic points (CPs). A closeness matrix representing the closeness of two samples in a same class is proposed in selecting CPs. By solving a number of constrained minimizations, the CPFIC is systematically built. Experiments were conducted on four classification problems with known Bayes errors, two benchmark classification problems, and a real-world application used in our research. The CPFIC performs well in accuracy evaluations in all the seven experiments. The summarizing abilities from the CPs into the linguistic descriptions of the fuzzy rule bases were also demonstrated in these examples Bayes errors. Characteristic point based fuzzy inference classifier. Closeness matrix. Fuzzy interpolation. Fuzzy rule base. IEEE Transactions on fuzzy systems 13, 5 (2005). FO UPD DENG-II Article