TY - JOUR
T1 - WSMOTER
T2 - a novel approach for imbalanced regression
AU - Camacho, Luís
AU - Bação, Fernando
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
https://doi.org/10.54499/UIDB/04152/2020#
Camacho, L., & Bação, F. (2024). WSMOTER: a novel approach for imbalanced regression. Applied Intelligence, 54(19), 8789-8799. https://doi.org/10.1007/s10489-024-05608-6 --- Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
PY - 2024/10
Y1 - 2024/10
N2 - Although the imbalanced learning problem is best known in the context of classification tasks, it also affects other areas of learning algorithms, such as regression. For regression, the problem is characterized by the existence of a continuous target variable domain and the need for models capable of making accurate predictions about rare events. Furthermore, such rare events with a real-value target are often the ones with greater interest in having models that can predict them. In this paper, we propose the novel approach WSMOTER (Weighting SMOTE for Regression) to tackle the imbalanced regression problem, which, according to the experimental work we present, outperforms currently available solutions to the problem.
AB - Although the imbalanced learning problem is best known in the context of classification tasks, it also affects other areas of learning algorithms, such as regression. For regression, the problem is characterized by the existence of a continuous target variable domain and the need for models capable of making accurate predictions about rare events. Furthermore, such rare events with a real-value target are often the ones with greater interest in having models that can predict them. In this paper, we propose the novel approach WSMOTER (Weighting SMOTE for Regression) to tackle the imbalanced regression problem, which, according to the experimental work we present, outperforms currently available solutions to the problem.
KW - Imbalanced
KW - Regression
KW - Data-level
KW - Supervised learning
UR - https://drive.google.com/drive/folders/1h6Q5sKB5bnqk0Kh01sH1uc6LTp4KSPbb?usp=sharing
UR - http://www.scopus.com/inward/record.url?scp=85197128685&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001255188700004
U2 - 10.1007/s10489-024-05608-6
DO - 10.1007/s10489-024-05608-6
M3 - Article
SN - 0924-669X
VL - 54
SP - 8789
EP - 8799
JO - Applied Intelligence
JF - Applied Intelligence
IS - 19
ER -