WSMOTER: a novel approach for imbalanced regression

Abstract

AbstractAlthough 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.