Fuzzy Classification using Self-Adaptive Algorithm to Generate Membership Function

Abstract

In Fuzzy classification, assigning (or constructing) membership function to sample data on the basis of their attributes is a vital task. In this paper an algorithm is proposed to generate membership function using genetic algorithm (GA). Correlation coefficient is used to select the attributes for generating membership function w.r.t. the class and to classify the data without any human expert's instructions. Membership function is initially assigned using historical data and then the shape and size is updated using BEX-PM (Thakur, 2014) genetic algorithm to classify the data. Proposed methodology tries to make use of lesser fuzzy rule. The performance of the method is compared with other existing methodology on the basis of accuracy rate to classify Iris, Wine and Pima data set.