The small sample imbalance (S&I) problem is a major challenge in machine learning and data analysis. It is characterized by a small number of samples and an imbalanced class distribution, which leads to poor model performance. In addition, indistinct inter-class feature distributions further complicate classification tasks. Existing methods often rely on algorithmic heuristics without sufficiently analyzing the underlying data characteristics. We argue that a detailed analysis from the data perspective is essential before developing an appropriate solution. Therefore, this paper proposes a systematic analytical framework for the S\&I problem. We first summarize imbalance metrics and complexity analysis methods, highlighting the need for interpretable benchmarks to characterize S&I problems. Second, we review recent solutions for conventional, complexity-based, and extreme S&I problems, revealing methodological differences in handling various data distributions. Our summary finds that resampling remains a widely adopted solution. However, we conduct experiments on binary and multiclass datasets, revealing that classifier performance differences significantly exceed the improvements achieved through resampling. Finally, this paper highlights open questions and discusses future trends.
View on arXiv@article{zhao2025_2504.14800,
title={ A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions },
author={ Shuxian Zhao and Jie Gui and Minjing Dong and Baosheng Yu and Zhipeng Gui and Lu Dong and Yuan Yan Tang and James Tin-Yau Kwok },
journal={arXiv preprint arXiv:2504.14800},
year={ 2025 }
}