针对回归任务中的数据分布不均问题，提出误差分布平滑方法提升模型性能。
Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
- 基于问题复杂度与数据密度设计新不平衡回归定义
- 通过误差分布平滑筛选代表性子集，减少冗余并保持平衡
- 适用于高复杂度区域数据稀缺的回归场景
在真实世界回归任务中，数据集常呈现分布不均现象：高复杂度区域数据稀少，低复杂度区域数据密集。现有分类方法因类别边界清晰而难以适用，且缺乏专门针对不平衡回归的解决方案。为此，本文提出新的不平衡回归概念，综合考虑问题复杂度与数据点密度，超越仅关注数据密度的传统定义。进一步提出误差分布平滑（EDS）方法，从数据集中有效选取代表性子集，降低冗余同时维持平衡与代表性。实验验证了EDS的有效性，相关代码与数据集可在 https://anonymous.4open.science/r/Error-Distribution-Smoothing-762F 获取。
原文摘要 · Abstract (English)
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 at https://anonymous.4open.science/r/Error-Distribution-Smoothing-762F.
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