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Erschienen in:
Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

26. Facing the Feature Selection Problem with a Binary PSO-GSA Approach

verfasst von : Malek Sarhani, Abdellatif El Afia, Rdouan Faizi

Erschienen in: Recent Developments in Metaheuristics

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Towards a Novel Reidentification Method Using Metaheuristics
Nächstes Kapitel An Optimal Deployment of Readers for RFID Network Planning Using NSGA-II
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Metadaten
Titel
Facing the Feature Selection Problem with a Binary PSO-GSA Approach
verfasst von
Malek Sarhani
Abdellatif El Afia
Rdouan Faizi
Copyright-Jahr
2018
Buch
Recent Developments in Metaheuristics

Print ISBN: 978-3-319-58252-8

Electronic ISBN: 978-3-319-58253-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-58253-5_26