Design and Applications of Fuzzy Classifier with Effective Feature Selection and Membership Function Optimization

[[abstract]]An efficient and simple decision support system must have the characteristics such as interpretable, easy understanding, convenient, et al. For this reason, the designed classifier in this study was based on a neuro-fuzzy network to combine the transparent characteristic of fuzzy system and learning ability of neural network. First, this study proposes a refined K-means algorithm and a gradient-based learning algorithm to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. Moreover, this study also uses grey relational algorithm to perform feature selection and proposes a novel feature reduction algorithm to overcome the drawbacks of grey relational algorithm. Because optimized processes contain complex and long steps, this study proposes a Fast Graph Fuzzy Classifier (FGFC) which has a novel determining scheme of the membership function degree and can prevent to confront an abstruse classifier algorithm as well as keep the advantages of the traditional fuzzy systems. All of the above-mentioned methods were implemented and analyzed in this study.