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ADASYN: Adaptive synthetic sampling approach for imbalanced learning

Haibo He, Yang Bai, Garcia, Edwardo A., Shutao Li·2008·2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
DOI 10.1109/ijcnn.2008.4633969
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Abstract

This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics.

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Research works printed but not linked: 27

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Metadata

Authors
He, H., Bai, Y., Garcia, E. A., & Li, S.
Venue
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
Publisher
IEEE
Published
2008-06
Pages
1322-1328
DOI
10.1109/ijcnn.2008.4633969
OpenAlex
W2104933073
Cited by
4,521
References
4
FWCI
4.22
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: ADASYN: Adaptive synthetic sampling approach for imbalanced learning. https://app.cochat.ai/papers/10.1109/ijcnn.2008.4633969
The paper
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). https://doi.org/10.1109/ijcnn.2008.4633969

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