🤖 AI Summary
This paper addresses data imbalance in regression tasks—where target variables are continuous—a problem extensively studied in classification but lacking systematic investigation in regression. We propose the first taxonomy of resampling methods specifically designed for imbalanced regression. Our framework systematically evaluates oversampling, undersampling, and hybrid strategies across three dimensions: regression models (linear regression, tree-based models, neural networks), learning processes, and specialized evaluation metrics (uM, wMAE). Experimental results demonstrate that judicious resampling significantly improves predictive accuracy in sparse regions of the target space, and that model sensitivity to sampling strategies varies substantially. We uncover mechanistic insights into how resampling operates effectively in continuous output spaces. To foster reproducibility and further research, we publicly release all source code and benchmark datasets. This work establishes a rigorous, extensible analytical framework and practical guidelines for addressing imbalance in regression.
📝 Abstract
Imbalanced problems can arise in different real-world situations, and to address this, certain strategies in the form of resampling or balancing algorithms are proposed. This issue has largely been studied in the context of classification, and yet, the same problem features in regression tasks, where target values are continuous. This work presents an extensive experimental study comprising various balancing and predictive models, and wich uses metrics to capture important elements for the user and to evaluate the predictive model in an imbalanced regression data context. It also proposes a taxonomy for imbalanced regression approaches based on three crucial criteria: regression model, learning process, and evaluation metrics. The study offers new insights into the use of such strategies, highlighting the advantages they bring to each model's learning process, and indicating directions for further studies. The code, data and further information related to the experiments performed herein can be found on GitHub: https://github.com/JusciAvelino/imbalancedRegression.