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Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods

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This paper presents a systematic review of data balancing strategies for machine learning, covering resampling and augmentation techniques. It categorizes methods from foundational approaches like SMOTE to advanced deep generative models and ensemble strategies. The review highlights that optimal method selection is highly dependent on dataset characteristics and evaluation metrics, and it identifies future research directions such as adapting foundation models to skewed distributions. AI

IMPACT Provides a comprehensive overview of techniques to improve model performance on imbalanced datasets, crucial for many real-world applications.

RANK_REASON This is a systematic review paper published on arXiv.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods

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COVERAGE [1]

  1. 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 ·

    Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods

    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…

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