Class Imbalance
Class Imbalance is a data distribution phenomenon in machine learning whereby the number of instances in one class significantly outweighs those in other classes, leading to potential biases in model training and performance, particularly in object detection and risk prediction tasks.
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2017–2026
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SMOTE: Synthetic Minority Over-sampling Technique
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research · 2002 · 31.2K citations · Full text
Haibo He, Edwardo A. Garcia · IEEE Transactions on Knowledge and Data Engineering · 2009 · 9.7K citations
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia et al. · 2008 · 4.4K citations
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Wrocław University of Science and Technology, Poland
24 publications · 5.2K citations
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Florida Atlantic University
Boca Raton, United States
44 h-index
254 publications
33.1K citations

Chinese Academy of Sciences
Beijing, China
41 h-index
225 publications
14.4K citations

Universidad de Granada
Granada, Spain
37 h-index
105 publications
22.7K citations
University of Chinese Academy of Sciences
Beijing, China
32 h-index
156 publications
13.5K citations

South China University of Technology
Guangzhou, China
30 h-index
118 publications
7.6K citations
Nanyang Technological University
Singapore, Singapore
28 h-index
68 publications
7.8K citations

Beihang University
Beijing, China
26 h-index
115 publications
4.7K citations
Tsinghua University
Beijing, China
26 h-index
86 publications
4.2K citations
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181 publications · 12.8K citations
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73 publications · 11.8K citations
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83 publications · 9.6K citations
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71 publications · 5.8K citations
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