A Novel Membership Function Definition for Fuzzy Classification

Abstract

In this paper, a novel membership function is defined for fuzzy sets using a supervised learning approach. Firstly, the training dataset is separated using the previously defined polyhedral conic functions in a supervised learning approach. Then obtained polyhedral conic functions are used for defining a new membership function. After that, a new fuzzy classification algorithm is formed to classify fuzzy sets with a similar structure. The algorithm with all suggested methods is implemented on real-world datasets, and the performance values are compared with the state of art classification algorithms.