%K Feature extraction; Intelligent computing; Nearest neighbor search; Population statistics, Algorithm accuracies; Benchmark datasets; Feature selection algorithm; Iteration numbers; K-nearest neighbor classifier; Optimizers; Population sizes, Iterative methods
%J 2020 International Conference on Computational Intelligence, ICCI 2020
%X 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.
%L scholars12629
%A R. Al-Wajih
%A S.J. Abdulakaddir
%A N.B.A. Aziz
%A Q. Al-Tashi
%P 130-136
%I Institute of Electrical and Electronics Engineers Inc.
%R 10.1109/ICCI51257.2020.9247792
%O cited By 4; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916
%T Binary Grey Wolf Optimizer with K-Nearest Neighbor classifier for Feature Selection
%D 2020