Hybrid Artificial Bees Colony and Particle Swarm on Feature Selection - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

Hybrid Artificial Bees Colony and Particle Swarm on Feature Selection

Hayet Djellali
  • Fonction : Auteur
  • PersonId : 1038467
Nacira Ghoualmi Zine
  • Fonction : Auteur
  • PersonId : 1038468
Nabiha Azizi
  • Fonction : Auteur
  • PersonId : 1038469

Résumé

This paper investigates feature selection method using two hybrid approaches based on artificial Bee colony ABC with Particle Swarm PSO algorithm (ABC-PSO) and ABC with genetic algorithm (ABC-GA). To achieve balance between exploration and exploitation a novel improvement is integrated in ABC algorithm. In this work, particle swarm PSO contribute in ABC during employed bees, and GA mutation operators are applied in Onlooker phase and Scout phase. It has been found that the proposed method hybrid ABC-GA method is competitive than exiting methods (GA, PSO, ABC) for finding minimal number of features and classifying WDBC, colon, hepatitis, DLBCL, lung cancer dataset. Experimental results are carried out on UCI data repository and show the effectiveness of mutation operators in term of accuracy and particle swarm for less size of features.
Fichier principal
Vignette du fichier
467079_1_En_9_Chapter.pdf (250.96 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01913902 , version 1 (07-11-2018)

Licence

Paternité

Identifiants

Citer

Hayet Djellali, Akila Djebbar, Nacira Ghoualmi Zine, Nabiha Azizi. Hybrid Artificial Bees Colony and Particle Swarm on Feature Selection. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.93-105, ⟨10.1007/978-3-319-89743-1_9⟩. ⟨hal-01913902⟩
169 Consultations
356 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More