lim-featureselectionusing-2021.pdf (1.25 MB)
Feature selection using enhanced particle swarm optimisation for classification models
journal contribution
posted on 2021-03-01, 00:00 authored by H Xie, L Zhang, Chee Peng LimChee Peng Lim, Y Yu, H LiuIn 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
History
Journal
SensorsVolume
21Issue
5Article number
1816Pagination
1 - 40Publisher
MDPI AGLocation
Basel, SwitzerlandPublisher DOI
Link to full text
ISSN
1424-8220eISSN
1424-8220Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Categories
Keywords
Science & TechnologyPhysical SciencesTechnologyChemistry, AnalyticalEngineering, Electrical & ElectronicInstruments & InstrumentationChemistryEngineeringfeature selectionevolutionary algorithmparticle swarm optimisationclassificationFEATURE SUBSET-SELECTIONDIFFERENTIAL EVOLUTIONGLOBAL OPTIMIZATIONLEUKEMIA DIAGNOSISPSOREGRESSIONALGORITHMPREDICTIONDistributed ComputingEcology
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC