An automatic approach for generation of fuzzy membership functions

Eliciting representative membership functions is one of the fundamental steps in applications of fuzzy theory. This paper investigates an unsupervised approach that incorporates variable bandwidth mean-shift and robust statistics for generating fuzzy membership functions. The approach automatically learns the number of representative functions from the underlying data distribution. Given a specific membership function, the approach then works out the associated parameters of the specific membership function. Our evaluation of the proposed approach consists of comparisons with two other techniques in terms of (i) parameterising MFs for attributes with different distributions, and (ii) classification performance of a fuzzy rule set that was developed using the parameterised output of these techniques. This evaluation involved its application using the trapezoidal and the triangular membership functions. Results demonstrate that the generated membership functions can better separate the underlying distributions and classifiers constructed using the proposed method of generating membership function outperformed three other classifiers that used different approaches for parameterisation of the attributes