2026-04-22T21:07:01Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/51142021-11-10T09:38:17Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Repositorio Institucional de la Universidad de Burgos
author
Kordos, Mirosław
author
Arnaiz González, Álvar
author
García Osorio, César
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.
eng
Attribution-NonCommercial-NoDerivatives 4.0 International
Prototype selection
Multi-output
Multi-target
Regression
Evolutionary prototype selection for multi-output regression
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/5114/1/Kordos-neurocomputing_2019.pdf
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https://riubu.ubu.es/bitstream/10259/5114/6/Kordos-neurocomputing_2019.pdf.txt
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Kordos-neurocomputing_2019.pdf.txt