Using the Grasshopper Optimization Algorithm for Fuzzy Classifier Design

Abstract

The paper describes three stages in the construction of a fuzzy classifier. The first refers to the formation of fuzzy rules, the second stage is feature selection, and the third stage is optimization of membership functions parameters. The influence of clustering methods on the efficiency of the formed fuzzy classifier rules was estimated by three different fitness functions. These functions were total variance, the Davies–Bouldin index, and the Calinski–Harabasz index. The grasshopper optimization algorithm was binarized using S- and V-shaped transformation functions for feature selection. The constructed classifiers have been tested on datasets from the KEEL repository.