Training fuzzy inference system-based classifiers with Krill Herd optimization

Abstract

In recent years, researchers have been able to access many novel metaheuristic algorithms inspired by natural phenomena. One such bio-inspired optimization routine is Krill Herd Algorithm (KHA). In this study, a new approach for modification of membership function parameters in a fuzzy inference system (FIS) is demonstrated. Here, the main intent is to compare KHA optimization with other heuristic and metaheuristic algorithms, as a means to train FIS structures. The proposed FIS training method has been designed to serve as a fuzzy classifier. Hence, benchmark data sets extracted from the University of California, Irvine (UCI) Machine Learning Repository were applied, while Classification Errors and Sum of Squared Errors were used as measures for evaluation criteria. The obtained results led to the conclusion that the utilization of KHA provides promising performance, especially in the case of imbalanced data—whether in terms of the classification measures or the time required for an adequate FIS training.