A Comprehensive Survey on Imbalanced Data Learning
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914032861052928 |
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| author | Gao, Xinyi Xie, Dongting Zhang, Yihang Wang, Zhengren Chen, Chong He, Conghui Yin, Hongzhi Zhang, Wentao |
| author_facet | Gao, Xinyi Xie, Dongting Zhang, Yihang Wang, Zhengren Chen, Chong He, Conghui Yin, Hongzhi Zhang, Wentao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08960 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on Imbalanced Data Learning Gao, Xinyi Xie, Dongting Zhang, Yihang Wang, Zhengren Chen, Chong He, Conghui Yin, Hongzhi Zhang, Wentao Machine Learning 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. |
| title | A Comprehensive Survey on Imbalanced Data Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.08960 |