A Comprehensive Survey on Imbalanced Data Learning

• URL: http://arxiv.org/abs/2502.08960v1
• Date: Thu, 13 Feb 2025 04:53:17 GMT
• Title: A Comprehensive Survey on Imbalanced Data Learning
• Authors: Xinyi Gao, Dongting Xie, Yihang Zhang, Zhengren Wang, Conghui He, Hongzhi Yin, Wentao Zhang,
• Abstract summary: imbalanced data is prevalent in various types of raw data and hinders the performance of machine learning. This survey systematically analyzes various real-world data formats. It concludes existing researches for different data formats into four categories: data re-balancing, feature representation, training strategy, and ensemble learning.
• Score: 45.3186824501823
• License:
• Abstract: 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 analyzing 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 help researchers comprehensively understand the pervasive nature of imbalance across diverse data format, 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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