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Imbalanced Data Problem in Machine Learning: A Review
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DOI：10.1109/ACCESS.2025.3531662.png)
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摘要
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One of the prominent challenges encountered in real-world data is an imbalance, characterized by unequal distribution of observations across different target classes, which complicates achieving accurate model classifications. This survey delves into various machine learning techniques developed to address the difficulties posed by imbalanced data. It discusses data-level methods such as oversampling and undersampling, algorithm-level solutions including ensemble learning and specific algorithm adjustments, cost-sensitive algorithms, and hybrid strategies that combine multiple approaches. Moreover, this paper emphasizes the crucial role of evaluation methods like Precision, F1 Score, Recall, G-mean, and AUC in measuring the effectiveness of these strategies under imbalanced conditions. A detailed review of recent research articles helps pinpoint persistent gaps in generalizability, scalability, and robustness across these methods, underscoring the necessity for ongoing improvements. The survey seeks to offer an extensive overview of current approaches that improve the efficiency and effectiveness of machine learning models dealing with imbalanced datasets, thus equipping researchers with the insights needed to develop robust and effective models ready for real-world application.
Keyword:
Data models
Machine learning
Classification algorithms
Machine learning algorithms
Training
Reviews
Fraud
Surveys
Ensemble learning
Data augmentation
Imbalanced data
machine learning
balance techniques
evaluation methods
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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