A Comprehensive Survey on Recent Metaheuristics for Feature Selection

DOKEROGLU, Tansel, DENIZ, Ayça and KIZILOZ, Hakan (2022). A Comprehensive Survey on Recent Metaheuristics for Feature Selection. Neurocomputing, 494, 269-296.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.neucom.2022.04.083
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    Abstract

    Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluation, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well. Keywords: Feature selection, Survey, Metaheuristic algorithms, Machine learning, Classification.

    Item Type: Article
    Uncontrolled Keywords: 08 Information and Computing Sciences; 09 Engineering; 17 Psychology and Cognitive Sciences; Artificial Intelligence & Image Processing
    Identification Number: https://doi.org/10.1016/j.neucom.2022.04.083
    Page Range: 269-296
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 26 Apr 2022 15:05
    Last Modified: 09 May 2022 10:29
    URI: http://shura.shu.ac.uk/id/eprint/30146

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