Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models. / Xie, Hailun; Zhang, Li; Lim, Chee Peng; Yu, Yonghong; Liu, Han.

In: Sensors, Vol. 21, No. 5, 1816, 05.03.2021.

Research output: Contribution to journalArticlepeer-review

Published
  • Hailun Xie
  • Li Zhang
  • Chee Peng Lim
  • Yonghong Yu
  • Han Liu

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.
Original languageEnglish
Article number1816
JournalSensors
Volume21
Issue number5
DOIs
Publication statusPublished - 5 Mar 2021
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43383932