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.
2018
Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César
Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning Journal Article
In: Expert Systems with Applications, vol. 109, pp. 114-130, 2018, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: Binary relevance, Label powerset, Multi-label classification, Prototype selection, RAkEL
@article{Arnaiz-González2018,
title = {Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning},
author = {Álvar Arnaiz-González and José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio},
url = {https://www.sciencedirect.com/science/article/pii/S0957417418303087},
doi = {10.1016/j.eswa.2018.05.017},
issn = {0957-4174},
year = {2018},
date = {2018-11-01},
journal = {Expert Systems with Applications},
volume = {109},
pages = {114-130},
abstract = {In this paper, the focus is on the application of prototype selection to multi-label data sets as a preliminary stage in the learning process. There are two general strategies when designing Machine Learning algorithms that are capable of dealing with multi-label problems: data transformation and method adaptation. These strategies have been successfully applied in obtaining classifiers and regressors for multi-label learning. Here we investigate the feasibility of data transformation in obtaining prototype selection algorithms for multi-label data sets from three prototype selection algorithms for single-label. The data transformation methods used were: binary relevance, dependent binary relevance, label powerset, and random k-labelsets. The general conclusion is that the methods of prototype selection obtained using data transformation are not better than those obtained through method adaptation. Moreover, prototype selection algorithms designed for multi-label do not do an entirely satisfactory job, because, although they reduce the size of the data set, without affecting significantly the accuracy, the classifier trained with the reduced data set does not improve the accuracy of the classifier when it is trained with the whole data set.},
keywords = {Binary relevance, Label powerset, Multi-label classification, Prototype selection, RAkEL},
pubstate = {published},
tppubtype = {article}
}
In this paper, the focus is on the application of prototype selection to multi-label data sets as a preliminary stage in the learning process. There are two general strategies when designing Machine Learning algorithms that are capable of dealing with multi-label problems: data transformation and method adaptation. These strategies have been successfully applied in obtaining classifiers and regressors for multi-label learning. Here we investigate the feasibility of data transformation in obtaining prototype selection algorithms for multi-label data sets from three prototype selection algorithms for single-label. The data transformation methods used were: binary relevance, dependent binary relevance, label powerset, and random k-labelsets. The general conclusion is that the methods of prototype selection obtained using data transformation are not better than those obtained through method adaptation. Moreover, prototype selection algorithms designed for multi-label do not do an entirely satisfactory job, because, although they reduce the size of the data set, without affecting significantly the accuracy, the classifier trained with the reduced data set does not improve the accuracy of the classifier when it is trained with the whole data set.