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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/4749

    Título
    Combining univariate approaches for ensemble change detection in multivariate data
    Autor
    Faithfull, William J. .
    Rodríguez Diez, Juan JoséUBU authority
    Kuncheva, Ludmila I. .
    Publicado en
    Information Fusion. 2019, V. 45, p. 202-214
    Editorial
    Elsevier
    Fecha de publicación
    2019-01
    ISSN
    1566-2535
    DOI
    10.1016/j.inffus.2018.02.003
    Abstract
    Detecting change in multivariate data is a challenging problem, especially when class labels are not available. There is a large body of research on univariate change detection, notably in control charts developed originally for engineering applications. We evaluate univariate change detection approaches —including those in the MOA framework — built into ensembles where each member observes a feature in the input space of an unsupervised change detection problem. We present a comparison between the ensemble combinations and three established ‘pure’ multivariate approaches over 96 data sets, and a case study on the KDD Cup 1999 network intrusion detection dataset. We found that ensemble combination of univariate methods consistently outperformed multivariate methods on the four experimental metrics.
    Materia
    Computer science
    Informática
    URI
    http://hdl.handle.net/10259/4749
    Versión del editor
    https://doi.org/10.1016/j.inffus.2018.02.003
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    Attribution-NonCommercial-NoDerivatives 4.0 International
    Documento(s) sujeto(s) a una licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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