Facing the feature selection problem with a binary PSO-GSA approach

TitreFacing the feature selection problem with a binary PSO-GSA approach
Publication TypeJournal Article
Year of Publication2018
AuthorsSarhani, M, A. Afia, E, Faizi, R
JournalOperations Research/ Computer Science Interfaces Series
Volume62
Pagination447-462
Abstract

Feature selection has become the focus of much research in many areas where we can face the problem of big data or complex relationship among features. Metaheuristics have gained much attention in solving many practical problems, including feature selection. Our contribution in this paper is to propose a binary hybrid metaheuristic to minimize a fitness function representing a trade-off between the classification error of selecting the feature subset and the corresponding number of features. This algorithm combines particle swarm optimization (PSO) and gravitational search algorithm (GSA). Also, a mutation operator is integrated to enhance population diversity. Experimental results on ten benchmark dataset show that our proposed hybrid method for feature selection can achieve high performance when comparing with other metaheuristic algorithms and well-known feature selection approaches. © Springer International Publishing AG 2018.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85032615163&doi=10.1007%2f978-3-319-58253-5_26&partnerID=40&md5=45163b8637f55b8af04f902f4f33afc2
DOI10.1007/978-3-319-58253-5_26
Revues: 

Partenaires

Localisation


Location map

Suivez-nous sur

  

Contactez-nous

ENSIAS

Avenue Mohammed Ben Abdallah Regragui, Madinat Al Irfane, BP 713, Agdal Rabat, Maroc

Résultat de recherche d'images pour "icone fax" Télécopie : (+212) 5 37 77 72 30

    Compteur de visiteurs:283,062
    Education - This is a contributing Drupal Theme
    Design by WeebPal.