WSMOTER: a novel approach for imbalanced regression

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Abstract

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.
Original languageEnglish
Pages (from-to)8789-8799
Number of pages11
JournalApplied Intelligence
Volume54
Issue number19
Early online date26 Jun 2024
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Imbalanced
  • Regression
  • Data-level
  • Supervised learning

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