In real-world regression tasks, datasets frequently exhibit imbalanced distributions, characterized by a scarcity of data in high-complexity regions and an abundance in low-complexity areas. This imbalance presents significant challenges for existing classification methods with clear class boundaries, while highlighting a scarcity of approaches specifically designed for imbalanced regression problems. To better address these issues, we introduce a novel concept of Imbalanced Regression, which takes into account both the complexity of the problem and the density of data points, extending beyond traditional definitions that focus only on data density. Furthermore, we propose Error Distribution Smoothing (EDS) as a solution to tackle imbalanced regression, effectively selecting a representative subset from the dataset to reduce redundancy while maintaining balance and representativeness. Through several experiments, EDS has shown its effectiveness, and the related code and dataset can be accessed atthis https URL.
View on arXiv@article{chen2025_2502.02277,
title={ Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression },
author={ Donghe Chen and Jiaxuan Yue and Tengjie Zheng and Lanxuan Wang and Lin Cheng },
journal={arXiv preprint arXiv:2502.02277},
year={ 2025 }
}