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  • Time-varying hierarchical chains of salps with random weight networks for feature selection

    Author(s)
    Faris, H
    Heidari, AA
    Al-Zoubi, AM
    Mafarja, M
    Aljarah, I
    Eshtay, M
    Mirjalili, S
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2020
    Metadata
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    Abstract
    Feature selection (FS) is considered as one of the most common and challenging tasks in Machine Learning. FS can be considered as an optimization problem that requires an efficient optimization algorithm to find its optimal set of features. This paper proposes a wrapper FS method that combines a time-varying number of leaders and followers binary Salp Swarm Algorithm (called TVBSSA) with Random Weight Network (RWN). In this approach, the TVBSSA is used as a search strategy, while RWN is utilized as an induction algorithm. The objective function is formulated in a manner to aggregate three objectives: maximizing the classification ...
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    Feature selection (FS) is considered as one of the most common and challenging tasks in Machine Learning. FS can be considered as an optimization problem that requires an efficient optimization algorithm to find its optimal set of features. This paper proposes a wrapper FS method that combines a time-varying number of leaders and followers binary Salp Swarm Algorithm (called TVBSSA) with Random Weight Network (RWN). In this approach, the TVBSSA is used as a search strategy, while RWN is utilized as an induction algorithm. The objective function is formulated in a manner to aggregate three objectives: maximizing the classification accuracy, maximizing the reduction rate of the selected features, and minimizing the complexity of generated RWN models. To assess the performance of the proposed approach, 20 well-known UCI datasets and a number of existing FS methods are employed. The comparative results show the ability of the proposed approach in outperforming similar algorithms in the literature and its merits to be used in systems that require FS.
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    Journal Title
    Expert Systems with Applications
    Volume
    140
    DOI
    https://doi.org/10.1016/j.eswa.2019.112898
    Subject
    Information and Computing Sciences
    Mathematical Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/387984
    Collection
    • Journal articles

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