%0 Conference Paper %1 pmlr-v94-branco18a %A Branco, Paula %A Torgo, Luis %A Ribeiro, Rita P. %B Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications %D 2018 %E Torgo, Luís %E Matwin, Stan %E Japkowicz, Nathalie %E Krawczyk, Bartosz %E Moniz, Nuno %E Branco, Paula %I PMLR %K imbalanced regression %P 67--81 %T REBAGG: REsampled BAGGing for Imbalanced Regression %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.

@inproceedings{pmlr-v94-branco18a, 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.}, added-at = {2022-01-19T10:01:29.000+0100}, author = {Branco, Paula and Torgo, Luis and Ribeiro, Rita P.}, biburl = {https://www.bibsonomy.org/bibtex/22832391bdd42667cbbb751101685ea50/msteininger}, booktitle = {Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications}, editor = {Torgo, Luís and Matwin, Stan and Japkowicz, Nathalie and Krawczyk, Bartosz and Moniz, Nuno and Branco, Paula}, interhash = {c7a3cc8c80dcce3b00c1a549bf129905}, intrahash = {2832391bdd42667cbbb751101685ea50}, keywords = {imbalanced regression}, month = {10 Sep}, pages = {67--81}, pdf = {http://proceedings.mlr.press/v94/branco18a/branco18a.pdf}, publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {2022-01-19T10:01:29.000+0100}, title = {REBAGG: REsampled BAGGing for Imbalanced Regression}, url = {https://proceedings.mlr.press/v94/branco18a.html}, volume = 94, year = 2018 }