%0 Conference Proceedings
%T Hybrid Artificial Bees Colony and Particle Swarm on Feature Selection
%+ Département d'Informatique [Annaba]
%A Djellali, Hayet
%A Djebbar, Akila
%A Zine, Nacira, Ghoualmi
%A Azizi, Nabiha
%Z Part 2: Evolutionary Computation
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA)
%C Oran, Algeria
%Y Abdelmalek Amine
%Y Malek Mouhoub
%Y Otmane Ait Mohamed
%Y Bachir Djebbar
%I Springer International Publishing
%3 Computational Intelligence and Its Applications
%V AICT-522
%P 93-105
%8 2018-05-08
%D 2018
%R 10.1007/978-3-319-89743-1_9
%K Genetic algorithm
%K Feature selection
%K Artificial Bees colony
%K Particle swarm
%K Hybrid ABC-GA
%K Hybrid ABC-PSO
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 5
%2 https://inria.hal.science/hal-01913902/document
%2 https://inria.hal.science/hal-01913902/file/467079_1_En_9_Chapter.pdf
%L hal-01913902
%U https://inria.hal.science/hal-01913902
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-CIIA
%~ IFIP-AICT-522