Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Xie, Hailun, Zhang, Li, Lim, Chee Peng, Yu, Yonghong and Liu, Han (2021) Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models. Sensors, 21 (5). p. 1816. ISSN 1424-8220

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

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.

Item Type: Article
Uncontrolled Keywords: feature selection, evolutionary algorithm, particle Swarm optimisation, classification
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 26 Feb 2021 09:08
Last Modified: 05 Mar 2021 12:45
URI: http://nrl.northumbria.ac.uk/id/eprint/45544

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