针对回归任务中的数据不平衡问题，系统分析了重采样策略的有效性。
Resampling strategies for imbalanced regression: a survey and empirical analysis
- 提出基于模型、学习过程和评估指标的不平衡回归分类体系。
- 实验证明重采样能显著提升模型在长尾分布下的预测性能。
- 适合关注回归中数据分布偏移问题的研究者与工程师参考。
不平衡问题在现实世界中广泛存在，为应对这一挑战，研究者提出了各类重采样或平衡算法。该问题在分类任务中已有广泛研究，但在回归任务中同样存在——目标值为连续变量。本文开展了广泛的实验研究，涵盖多种平衡策略与预测模型，并采用多维度指标评估模型在不平衡回归数据上的表现。同时，提出了一种基于三个关键标准（回归模型、学习过程、评估指标）的不平衡回归方法分类体系。研究揭示了不同策略对模型学习过程的增益，指明了未来研究方向。相关代码、数据及实验细节可于GitHub获取：https://github.com/JusciAvelino/imbalancedRegression。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。