系统梳理数据不平衡问题的四大解决方法，助力机器学习模型更公平准确。
A Comprehensive Survey on Imbalanced Data Learning
- 将不平衡数据处理分为重采样、特征表示、训练策略和集成学习四类。
- 总结了各类方法在真实数据中的适用场景与效果差异。
- 适合研究数据偏斜问题的算法工程师与学术研究人员参考。
随着数据可用性的扩大，机器学习在学术界和工业界均取得显著突破。然而，各类原始数据中普遍存在数据分布不均衡问题，严重干扰模型决策过程，制约性能提升。为深入理解数据不平衡现象并推动相关研究与应用，本文系统分析多种真实数据形式，将现有研究成果归纳为四类：数据重平衡、特征表示、训练策略和集成学习。该结构化分析有助于研究者全面把握不平衡现象在不同数据类型中的普遍性，明确实现特定研究目标的路径。文章还概述了相关开源工具库，揭示当前挑战，并提出新见解，以促进该关键领域的未来发展。
原文摘要 · Abstract (English)
With the expansion of data availability, machine learning (ML) has achieved remarkable breakthroughs in both academia and industry. However, imbalanced data distributions are prevalent in various types of raw data and severely hinder the performance of ML by biasing the decision-making processes. To deepen the understanding of imbalanced data and facilitate the related research and applications, this survey systematically analyzes various real-world data formats and concludes existing researches for different data formats into four distinct categories: data re-balancing, feature representation, training strategy, and ensemble learning. This structured analysis helps researchers comprehensively understand the pervasive nature of imbalance across diverse data formats, thereby paving a clearer path toward achieving specific research goals. We provide an overview of relevant open-source libraries, spotlight current challenges, and offer novel insights aimed at fostering future advancements in this critical area of study.
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