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<p>Imbalanced regression refers to regression problems where the target variable has a non-uniform distribution. Classical approaches to address this issue include weighted regression and resampling techniques. While weighted regression modifies the learning algorithm, resampling methods adjust the data distribution, making them more flexible and commonly used. However, resampling methods are sensitive to noise, particularly in overlapping regions between rare and frequent instances, which can lead to the generation of misleading synthetic samples. In this context, noise refers to rare instances that resemble non-rare ones in feature space or non-rare instances that exhibit rare-like features, making resampling less effective. In this study, we propose RA-HyRL designed to address the noise problem in imbalanced regression. RA-HyRL is a hybrid method that performs both oversampling and undersampling. It leverages boosting weights to identify noisy and informative samples, and selects instances to resample based on their relevance. The number of neighbors used for synthetic sample generation is also automatically determined for each instance. We evaluate the proposed method against six existing resampling techniques on 30 datasets using 10 regression models. Statistical tests demonstrate that RA-HyRL outperforms the alternatives at rareness thresholds of 0.5 and 0.8, according to the SERA metric. The source code is available at: <a href="https://github.com/fatihsaglam/ImbRegSamp.">https://github.com/fatihsaglam/ImbRegSamp.</a></p>

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RA-HyRL: a robust hybrid resampling method for imbalanced regression with instance labeling

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Fatih Sağlam,
  • Fatima Ulubayova

摘要

Imbalanced regression refers to regression problems where the target variable has a non-uniform distribution. Classical approaches to address this issue include weighted regression and resampling techniques. While weighted regression modifies the learning algorithm, resampling methods adjust the data distribution, making them more flexible and commonly used. However, resampling methods are sensitive to noise, particularly in overlapping regions between rare and frequent instances, which can lead to the generation of misleading synthetic samples. In this context, noise refers to rare instances that resemble non-rare ones in feature space or non-rare instances that exhibit rare-like features, making resampling less effective. In this study, we propose RA-HyRL designed to address the noise problem in imbalanced regression. RA-HyRL is a hybrid method that performs both oversampling and undersampling. It leverages boosting weights to identify noisy and informative samples, and selects instances to resample based on their relevance. The number of neighbors used for synthetic sample generation is also automatically determined for each instance. We evaluate the proposed method against six existing resampling techniques on 30 datasets using 10 regression models. Statistical tests demonstrate that RA-HyRL outperforms the alternatives at rareness thresholds of 0.5 and 0.8, according to the SERA metric. The source code is available at: https://github.com/fatihsaglam/ImbRegSamp.