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  • Binary Dragonfly Algorithm for Feature Selection

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    MafarjaPUB3224.pdf (263.5Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Mafarja, Majdi M
    Eleyan, Derar
    Jaber, Iyad
    Mirjalili, Seyedali
    Hammouri, Abdelaziz
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2017
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    Abstract
    Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. Dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization (PSO), Genetic Algorithms (GAs) in terms of classification accuracy and number of ...
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    Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. Dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization (PSO), Genetic Algorithms (GAs) in terms of classification accuracy and number of selected attributes. The results show the ability of Binary Dragonfly Algorithm (BDA) in searching the feature space and selecting the most informative features for classification tasks.
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    Conference Title
    2017 INTERNATIONAL CONFERENCE ON NEW TRENDS IN COMPUTING SCIENCES (ICTCS)
    Volume
    2018-January
    DOI
    https://doi.org/10.1109/ICTCS.2017.43
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Evolutionary computation
    Publication URI
    http://hdl.handle.net/10072/378086
    Collection
    • Conference outputs

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