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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/5114

    Título
    Evolutionary prototype selection for multi-output regression
    Autor
    Kordos, Mirosław
    Arnaiz González, ÁlvarUBU authority Orcid
    García Osorio, CésarUBU authority Orcid
    Publicado en
    Neurocomputing. 2019, V. 358, p. 309-320
    Editorial
    Elsevier
    Fecha de publicación
    2019
    ISSN
    0925-2312
    DOI
    10.1016/j.neucom.2019.05.055
    Abstract
    A novel approach to prototype selection for multi-output regression data sets is presented. A multi-objective evolutionary algorithm is used to evaluate the selections using two criteria: training data set compression and prediction quality expressed in terms of root mean squared error. A multi-target regressor based on k-NN was used for that purpose during the training to evaluate the error, while the tests were performed using four different multi-target predictive models. The distance matrices used by the multi-target regressor were cached to accelerate operational performance. Multiple Pareto fronts were also used to prevent overfitting and to obtain a broader range of solutions, by using different probabilities in the initialization of populations and different evolutionary parameters in each one. The results obtained with the benchmark data sets showed that the proposed method greatly reduced data set size and, at the same time, improved the predictive capabilities of the multi-output regressors trained on the reduced data set.
    Palabras clave
    Prototype selection
    Multi-output
    Multi-target
    Regression
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/5114
    Versión del editor
    https://doi.org/10.1016/j.neucom.2019.05.055
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