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Advanced Technology and Social Media Influence on Research, Industry and Community Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Hybrid Artificial Bees Colony and Particle Swarm on Feature Selection

Authors : Hayet Djellali, Akila Djebbar, Nacira Ghoualmi Zine, Nabiha Azizi

Published in: Computational Intelligence and Its Applications

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
Hybrid Artificial Bees Colony and Particle Swarm on Feature Selection
Authors
Hayet Djellali
Akila Djebbar
Nacira Ghoualmi Zine
Nabiha Azizi
Copyright Year
2018
Book
Computational Intelligence and Its Applications

Print ISBN: 978-3-319-89742-4

Electronic ISBN: 978-3-319-89743-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-89743-1_9

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