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Feature selection using enhanced particle swarm optimisation for classification models.

Xie, Hailun; Zhang, Li; Lim, Chee Peng; Yu, Yonghong; Liu, Han


Hailun Xie

Li Zhang

Chee Peng Lim

Yonghong Yu

Han Liu


In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.


XIE, H., ZHANG, L., LIM, C.P., YU, Y. and LIU, H. 2021. Feature selection using enhanced particle swarm optimisation for classification models. Sensors [online], 21(5), article 1816. Available from:

Journal Article Type Article
Acceptance Date Feb 22, 2021
Online Publication Date Mar 5, 2021
Publication Date Mar 31, 2021
Deposit Date Mar 15, 2021
Publicly Available Date Mar 15, 2021
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 21
Issue 5
Article Number 1816
Keywords Feature selection; Evolutionary algorithm; Particle swarm optimisation; Classification
Public URL


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