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  • Asynchronous accelerating multi-leader salp chains for feature selection

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    MirjaliliPUB362.pdf (1.054Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Aljarah, Ibrahim
    Mafarja, Majdi
    Heidari, Ali Asghar
    Faris, Hossam
    Zhang, Yong
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2018
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    Abstract
    Feature selection is an imperative preprocessing step that can positively affect the performance of machine learning techniques. Searching for the optimal feature subset amongst an unabridged dataset is a challenging problem, especially for large-scale datasets. In this research, a binary Salp Swarm Algorithm (SSA) with asynchronous updating rules and a new leadership structure is proposed. To set the best leadership structure, several extensive experiments are performed to determine the most effective number of leaders in the social organization of the artificial salp chain. Inspired from the behavior of a termite colony ...
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    Feature selection is an imperative preprocessing step that can positively affect the performance of machine learning techniques. Searching for the optimal feature subset amongst an unabridged dataset is a challenging problem, especially for large-scale datasets. In this research, a binary Salp Swarm Algorithm (SSA) with asynchronous updating rules and a new leadership structure is proposed. To set the best leadership structure, several extensive experiments are performed to determine the most effective number of leaders in the social organization of the artificial salp chain. Inspired from the behavior of a termite colony (TC) in dividing the termites into four types, the salp chain is then divided into several sub-chains, where the salps in each sub-chain can follow a different strategy to adaptively update their locations. Three different updating strategies are employed in this paper. The proposed algorithm is tested and validated on 20 well-known datasets from the UCI repository. The results and comparisons verify that utilizing half of the salps as leaders of the chain can significantly improve the performance of SSA in terms of accuracy metric. Furthermore, dynamically tuning the single parameter of algorithm enable it to more effectively explore the search space in dealing with different feature selection datasets.
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    Journal Title
    Applied Soft Computing
    Volume
    71
    DOI
    https://doi.org/10.1016/j.asoc.2018.07.040
    Copyright Statement
    © 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Information Systems not elsewhere classified
    Applied Mathematics
    Artificial Intelligence and Image Processing
    Information Systems
    Publication URI
    http://hdl.handle.net/10072/382426
    Collection
    • Journal articles

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