RT info:eu-repo/semantics/article
T1 Evolutionary prototype selection for multi-output regression
A1 Kordos, Mirosław
A1 Arnaiz González, Álvar
A1 García Osorio, César
K1 Prototype selection
K1 Multi-output
K1 Multi-target
K1 Regression
K1 Informática
K1 Computer science
AB 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.
PB Elsevier
SN 0925-2312
YR 2019
FD 2019
LK http://hdl.handle.net/10259/5114
UL http://hdl.handle.net/10259/5114
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 25-abr-2024