relation: https://khub.utp.edu.my/scholars/12629/
title: Binary Grey Wolf Optimizer with K-Nearest Neighbor classifier for Feature Selection
creator: Al-Wajih, R.
creator: Abdulakaddir, S.J.
creator: Aziz, N.B.A.
creator: Al-Tashi, Q.
description: Iteration number and population size are two key factors that influence the effectiveness of a certain feature selection algorithm. Randomly choosing these factors, however, might be an impractical approach that could lead to low algorithm accuracy. In this paper, we assessed the changes in the accuracy of Binary Grey Wolf Optimizer (BGWO) at varying a function of iteration number (50,100,150 and 200) and population size (10,20,30) in four benchmark datasets. The results generally indicate that there is an optimum iteration number (T) beyond which the accuracy of BGWO started to decrease. Similarly, it was seen that an optimum population size (N) exists, which yield a high average accuracy of the BGWO algorithm. The findings suggest that it is essential to optimize the iteration number and population size before the execution of BGWO. © 2020 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2020
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Al-Wajih, R. and Abdulakaddir, S.J. and Aziz, N.B.A. and Al-Tashi, Q.  (2020) Binary Grey Wolf Optimizer with K-Nearest Neighbor classifier for Feature Selection.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097552219&doi=10.1109%2fICCI51257.2020.9247792&partnerID=40&md5=9399b4c26698006e1581b88d8f72ab1f
relation: 10.1109/ICCI51257.2020.9247792
identifier: 10.1109/ICCI51257.2020.9247792