Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Jomana Yousef Khaseeb
Palestine Technical University/Kadoorie
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
A hybrid swarm intelligence feature selection approach based on time-varying transition parameter Jomana Yousef Khaseeb; Arabi Keshk; Anas Youssef
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp781-795

Abstract

Feature selection aims to reduce the dimensionality of a dataset by removing superfluous attributes. This paper proposes a hybrid approach for feature selection problem by combining particle swarm optimization (PSO), grey wolf optimization (GWO), and tournament selection (TS) mechanism. Particle swarm enhances the diversification at the beginning of the search mechanism, grey wolf enhances the intensification at the end of the search mechanism, while tournament selection maintains diversification not only at the beginning but also at the end of the search process to achieve local optima avoidance. A time-varying transition parameter and a random variable are used to select either particle swarm, grey wolf, or tournament selection techniques during search process. This paper proposes different variants of this approach based on S-shaped and V-shaped transfer functions (TFs) to convert continuous solutions to binaries. These variants are named hybrid tournament grey wolf particle swarm (HTGWPS), followed by S or V letter to indicate the TF type, and followed by the TF’s number. These variants were evaluated using nine high-dimensional datasets. The results revealed that HTGWPS-V1 outperformed other V’s variants, PSO, and GWO on 78% of the datasets based on maximum classification accuracy obtained by a minimal feature subset. Also, HTGWPS-V1 outperformed six well-known-metaheuristics on 67% of the datasets.
Co-Authors Anas Youssef Arabi Keshk