Double groups of gates based Takagi-Sugeno-Kang (DG-TSK) fuzzy system for simultaneous feature selection and rule extraction

Abstract

Embedded feature selection methods based on fuzzy model have been well studied, in which the gate functions are usually used to distinguish the essential features. Fuzzy rules play a critical role in constructing fuzzy systems. However, few works simultaneously evaluate the quality of fuzzy rules during the fuzzy system based feature selection approaches. This article proposes double groups of gates based Takagi-Sugeno-Kang (DG-TSK) fuzzy model to simultaneously conduct feature selection and rule extraction in one training phase. Firstly, a novel gate function is introduced, which is embedded into the system. We call this function as M-gate since it is shaped like an M. Secondly, a new type of fuzzy rule base (FRB) is designed by transforming each data point into one fuzzy rule, which is named point-based FRB (P-FRB). Based on the M-gate and P-FRB, DG-TSK model is developed, in which M-gates are embedded in the antecedents for feature selection and consequents for rule extraction. Note that these two procedures are performed at the same time. We test DG-TSK on 18 classification datasets and compare it with other similar methods. The experiment results demonstrate that DG-TSK is effective.