Feature selection using enhanced particle swarm optimisation for classification models.

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

Abstract

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.

Citation

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: https://doi.org/10.3390/s21051816

• 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
• Electronic ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 5
• Article Number: 1816
• DOI: https://doi.org/10.3390/s21051816
• Keywords: Feature selection; Evolutionary algorithm; Particle swarm optimisation; Classification
• Public URL: https://rgu-repository.worktribe.com/output/1268940