REBAGG: REsampled BAGGing for Imbalanced Regression

Paula Branco, Luis Torgo, Rita P. Ribeiro
Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 94:67-81, 2018.

Abstract

The problem of imbalanced domains is important in multiple real world applications. This problem has been thoroughly studied for classification tasks. In particular, the adaptation of ensembles to tackle imbalanced domains has shown important advantages in a classification context. Still, for imbalanced regression problems only a few solutions exist. Moreover, the capabilities of ensembles for dealing with imbalanced regression tasks is yet to be explored. In this paper we present the REsampled BAGGing (REBAGG) algorithm, a bagging-based ensemble method that incorporates data pre-processing strategies for addressing imbalanced domains in regression tasks. The extensive experimental evaluation conducted shows the advantage of our proposal in a diverse set of domains and learning algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v94-branco18a, title = {REBAGG: REsampled BAGGing for Imbalanced Regression}, author = {Branco, Paula and Torgo, Luis and Ribeiro, Rita P.}, booktitle = {Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications}, pages = {67--81}, year = {2018}, editor = {Torgo, Luís and Matwin, Stan and Japkowicz, Nathalie and Krawczyk, Bartosz and Moniz, Nuno and Branco, Paula}, volume = {94}, series = {Proceedings of Machine Learning Research}, month = {10 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v94/branco18a/branco18a.pdf}, url = {https://proceedings.mlr.press/v94/branco18a.html}, abstract = {The problem of imbalanced domains is important in multiple real world applications. This problem has been thoroughly studied for classification tasks. In particular, the adaptation of ensembles to tackle imbalanced domains has shown important advantages in a classification context. Still, for imbalanced regression problems only a few solutions exist. Moreover, the capabilities of ensembles for dealing with imbalanced regression tasks is yet to be explored. In this paper we present the REsampled BAGGing (REBAGG) algorithm, a bagging-based ensemble method that incorporates data pre-processing strategies for addressing imbalanced domains in regression tasks. The extensive experimental evaluation conducted shows the advantage of our proposal in a diverse set of domains and learning algorithms.} }
Endnote
%0 Conference Paper %T REBAGG: REsampled BAGGing for Imbalanced Regression %A Paula Branco %A Luis Torgo %A Rita P. Ribeiro %B Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications %C Proceedings of Machine Learning Research %D 2018 %E Luís Torgo %E Stan Matwin %E Nathalie Japkowicz %E Bartosz Krawczyk %E Nuno Moniz %E Paula Branco %F pmlr-v94-branco18a %I PMLR %P 67--81 %U https://proceedings.mlr.press/v94/branco18a.html %V 94 %X The problem of imbalanced domains is important in multiple real world applications. This problem has been thoroughly studied for classification tasks. In particular, the adaptation of ensembles to tackle imbalanced domains has shown important advantages in a classification context. Still, for imbalanced regression problems only a few solutions exist. Moreover, the capabilities of ensembles for dealing with imbalanced regression tasks is yet to be explored. In this paper we present the REsampled BAGGing (REBAGG) algorithm, a bagging-based ensemble method that incorporates data pre-processing strategies for addressing imbalanced domains in regression tasks. The extensive experimental evaluation conducted shows the advantage of our proposal in a diverse set of domains and learning algorithms.
APA
Branco, P., Torgo, L. & Ribeiro, R.P.. (2018). REBAGG: REsampled BAGGing for Imbalanced Regression. Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, in Proceedings of Machine Learning Research 94:67-81 Available from https://proceedings.mlr.press/v94/branco18a.html.

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