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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/6192

    Título
    When is resampling beneficial for feature selection with imbalanced wide data?
    Autor
    Ramos Pérez, Ismael
    Arnaiz González, ÁlvarAutoridad UBU Orcid
    Rodríguez Diez, Juan JoséAutoridad UBU
    García Osorio, CésarAutoridad UBU Orcid
    Publicado en
    Expert Systems with Applications. 2022, V. 188, 116015
    Editorial
    Elsevier
    Fecha de publicación
    2022-02
    ISSN
    0957-4174
    DOI
    10.1016/j.eswa.2021.116015
    Résumé
    This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests. Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS.
    Palabras clave
    Feature selection
    Wide data
    High dimensional data
    Very low sample size
    Unbalanced
    Machine learning
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/6192
    Versión del editor
    https://doi.org/10.1016/j.eswa.2021.116015
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