2019
Kordos, Mirosław; Arnaiz-González, Álvar; García-Osorio, César
Evolutionary prototype selection for multi-output regression Journal Article
In: Neurocomputing, vol. 358, pp. 309-320, 2019, ISSN: 0925-2312.
Abstract | Links | BibTeX | Tags: Multi-output, Multi-target, Prototype selection, Regression, SELECTED
@article{Kordos2019,
title = {Evolutionary prototype selection for multi-output regression},
author = {Mirosław Kordos and Álvar Arnaiz-González and César García-Osorio},
url = {https://www.sciencedirect.com/science/article/pii/S0925231219307611?fbclid=IwAR1qb5kLk1-PyqfAPprRnb6Jv75rMgJS3dY1rDqWF610G2lCttEW3QIBU4c},
doi = {10.1016/j.neucom.2019.05.055},
issn = {0925-2312},
year = {2019},
date = {2019-09-17},
journal = {Neurocomputing},
volume = {358},
pages = {309-320},
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.},
keywords = {Multi-output, Multi-target, Prototype selection, Regression, SELECTED},
pubstate = {published},
tppubtype = {article}
}
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.