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Título : Evolutionary prototype selection for multi-output regression
Autor : Kordos, Mirosław
Arnaiz González, Álvar
García Osorio, César
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
Resumen : 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
Materia: Informática
Computer science
Licencia: https://creativecommons.org/licenses/by-nc-nd/4.0/
URI : http://hdl.handle.net/10259/5114
Versión del editor: https://doi.org/10.1016/j.neucom.2019.05.055
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