Classification of Multi-Class Datasets Using 2D Membership Functions in TSK Fuzzy System

Abstract

Fuzzy modeling of high-dimensional systems is a challenging topic. This study proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. The proposed method works on the fuzzification layer and tries to use two-dimensional membership functions instead of onedimensional ones. This approach reduces fuzzy rule base radically due to using of two-dimensional membership functions which lead to reduction of parameters. The resulting fuzzy system generated by this method has the following distinct features: 1) the fuzzy system is quite simplified; 2) the fuzzy system is interpretable; 3) the dependencies between the inputs and the outputs are clearly shown. This method has successfully been applied to three classification problem and the results are compared with other works.