English(EN) Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods

数据平衡策略：重采样和增强方法的系统性调查

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-30 04:00

本文对机器学习中的数据平衡策略进行了系统性回顾，涵盖了重采样和增强技术。它将方法从 SMOTE 等基础方法归类到先进的深度生成模型和集成策略。回顾强调，最佳方法的选择高度依赖于数据集特征和评估指标，并指出了未来的研究方向，例如将基础模型适应偏斜分布。 AI

影响全面概述了用于改善模型在不平衡数据集上性能的技术，这对于许多实际应用至关重要。

排序理由这是一篇发表在 arXiv 上的系统性综述论文。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Research; 这是一篇发表在 arXiv 上的系统性综述论文。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 120 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

arXiv stat.ML TIER_1 English(EN) · Behnam Yousefimehr, Mehdi Ghatee, Javad Fazli, Shervin Ghaffari, Zahra Rafei, Mohammad Amin Seifi, Sajed Tavakoli, Abolfazl Nikahd, Mahdi Razi Gandomani, Alireza Orouji, Ramtin Mahmoudi Kashani, Sarina Heshmati, Negin Sadat Mousavi · 2026-04-30 04:00

数据平衡策略：重采样和增强方法的系统性调查

arXiv:2505.13518v2 Announce Type: replace Abstract: Imbalanced datasets, where one class significantly outnumbers others, remain a persistent challenge in machine learning, often biasing predictions toward the majority class and degrading classifier performance. This paper provid…