AI 中文总结
针对不平衡回归问题，提出基于实例硬度的相关性函数InHaR，综合考虑目标分布与学习难度，能在双峰分布下准确识别罕见区域，用于指导重采样策略时可显著提升预测性能。
AI 中文摘要
不平衡回归问题在目标变量分布不对称时出现，导致数据集中值范围代表性不足。传统识别罕见实例的方法依赖相关性函数，但在双峰分布等复杂场景中存在局限。本研究提出基于实例硬度的相关性函数（InHaR），它不仅考虑目标分布，还纳入学习难度。实验表明，InHaR能在双峰分布下正确识别罕见区域，用于指导重采样策略时可显著提升预测性能。相关代码、数据集等公开可用。
英文摘要
Imbalanced regression problems arise when the target variable has an asymmetric distribution, resulting in underrepresented value ranges in the dataset. Traditional approaches for identifying rare instances rely on a relevance function that assigns higher importance to specific regions of the target distribution. However, the effectiveness of imbalance-aware learning methods depends strongly on how relevance is defined. In more complex scenarios, such as bimodal distributions, traditional relevance functions struggle to capture rarity, as they assign fixed relevance values based solely on target values, thereby compromising the distinction between truly rare and normal instances. To address these limitations, this study proposes an Instance Hardness-based relevance function (InHaR) for identifying rare instances in regression problems. Unlike traditional relevance functions, the proposed approach incorporates learning difficulty, allowing rarity to be inferred not only from the target distribution but also from the difficulty of instances for the learning algorithm. This property is particularly important in bimodal scenarios, where rarity cannot be accurately inferred from target values alone. Experimental results demonstrate that the InHaR correctly identifies rare regions under bimodal distributions and, when used to guide resampling strategies such as Random Oversampling (RO) and Gaussian Noise (GN), leads to significant improvements in predictive performance compared to traditional relevance-based approaches. The code, dataset, and further details about the proposed method are publicly available at https://github.com/VitorLeitao/instance-hardness-Imbalanced-regression.
CommentsPaper accepted to the 2026 International Joint Conference on Neural Networks
