Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers

Xue, YuTang, TaoPang, WeiLiu, Alex X2020-12-232020-12-232020-03Xue, Y, Tang, T, Pang, W & Liu, A X 2020, 'Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers', Applied Soft Computing, vol. 88, 106031. https://doi.org/10.1016/j.asoc.2019.1060311568-4946ORCID: /0000-0002-1761-6659/work/79064089https://hdl.handle.net/2164/15557This work was partially supported by the National Natural Science Foundation of China (61403206, 61876089,61876185), the Natural Science Foundation of Jiangsu Province (BK20141005), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJB520025), the Engineering Research Center of Digital Forensics, Ministry of Education, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.126095071engparticle swarm optimizationfeature selectionlarge-scale problemsself-adaptiveclassificationENSEMBLEFeature selectionINFORMATIONSelf-adaptiveCLASSIFICATIONParticle swarm optimizationLarge-scale problemsGENETIC ALGORITHMSCHEMEDIFFERENTIAL EVOLUTION ALGORITHMClassificationMACHINEQA75 Electronic computers. Computer scienceSoftwareQA75Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiersJournal article10.1016/j.asoc.2019.106031http://www.scopus.com/inward/record.url?scp=85077051695&partnerID=8YFLogxK88