2026-04-22T17:36:33Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/51142021-11-10T09:38:17Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Kordos, Mirosław b9e7fba7-1c59-4e9d-845a-838ca250ed7a 500 Arnaiz González, Álvar 39 500 0000-0001-6965-0237 García Osorio, César 212 500 0000-0002-1206-1084 2019-06-07T11:14:53Z 2019-06-07T11:14:53Z 2019 0925-2312 http://hdl.handle.net/10259/5114 10.1016/j.neucom.2019.05.055 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. NCN (Polish National Science Center) grant “Evolutionary Methods in Data Selection” No. 2017/01/X/ST6/00202, project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, and project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León cofinanced with European Union FEDER funds. application/pdf eng Elsevier Neurocomputing. 2019, V. 358, p. 309-320 https://doi.org/10.1016/j.neucom.2019.05.055 info:eu-repo/grantAgreement/MINECO/TIN2015-67534-P info:eu-repo/grantAgreement/JCyL/BU085P17 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Prototype selection Multi-output Multi-target Regression Informática Computer science Evolutionary prototype selection for multi-output regression info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/5114/8/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 8 THUMBNAIL Kordos-neurocomputing_2019.pdf.jpg Kordos-neurocomputing_2019.pdf.jpg IM Thumbnail image/jpeg 5056 https://riubu.ubu.es/bitstream/10259/5114/7/Kordos-neurocomputing_2019.pdf.jpg 60d32d6488fde1e9b13a347ae500853f MD5 7 ORIGINAL Kordos-neurocomputing_2019.pdf Kordos-neurocomputing_2019.pdf application/pdf 894296 https://riubu.ubu.es/bitstream/10259/5114/1/Kordos-neurocomputing_2019.pdf a51d29e983dd866aaa06fd7834e5dd65 MD5 1 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/5114/5/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 5 TEXT Kordos-neurocomputing_2019.pdf.txt Kordos-neurocomputing_2019.pdf.txt Extracted text text/plain 78734 https://riubu.ubu.es/bitstream/10259/5114/6/Kordos-neurocomputing_2019.pdf.txt 7677791dd7c92b7281ad1040785a495f MD5 6 10259/5114 oai:riubu.ubu.es:10259/5114 2021-11-10 10:38:17.293 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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