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

Mohd Najib Mohd Salleh, Kashif Hussain, Noreen Talpur

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publicationIntelligent and Interactive Computing
Subtitle of host publicationProceedings of IIC 2018
EditorsVincenzo Piuri, Valentina Emilia Balas, Samarjeet Borah, Sharifah Sakinah Syed Ahmad
PublisherSpringer Singapore
ISBN (Electronic)978-981-13-6031-2
ISBN (Print)978-981-13-6030-5
Publication statusPublished - 17 May 2019

Publication series

NameLecture Notes in Networks and Systems

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