A Divide-and-Conquer Strategy for Adaptive Neuro-Fuzzy Inference System Learning Using Metaheuristic Algorithm

Abstract

Adaptive neuro-fuzzy inference system (ANFIS) has produced promising results in model approximation. The core of ANFIS computation lies in the training of its parameters. Metaheuristic algorithms have been successfully employed on ANFIS parameters training. Conventionally, a population individual in metaheuristic algorithm, considered as ANFIS model with candidate parameters, is evaluated for its fitness on complete training set. This makes ANFIS parameters training computationally expensive when dataset is large. This paper proposes divide-and-conquer strategy where each population individual is given a piece of dataset instead of complete dataset to train and evaluate ANFIS model fitness. The proposed ANFIS training approach is evaluated on accuracy on testing dataset, as well as, training computational complexity. Experiments on several classification problems reveal that the proposed methodology reduced training computational complexity up to 93%. Moreover, the proposed ANFIS training approach generated rules that achieved better accuracy on testing dataset as compared to conventional training approach.