The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance
Abstract
Abstract Imbalanced classification problems are often encountered in many applications. The challenge is that there is a minority class that has typically very little data and is often the focus of attention. One approach for handling imbalance is to generate extra data from the minority class, to overcome its shortage of data. The Synthetic Minority over-sampling TEchnique (SMOTE) is one of the dominant methods in the literature that achieves this extra sample generation. It is based on generating examples on the lines connecting a point and one its K -nearest neighbors. This paper presents a theoretical and experimental analysis of the SMOTE method. We explore the accuracy of how faithful it emulates the underlying density. To our knowledge, this is the first mathematical analysis of the SMOTE method. Moreover, we analyze the effect of the different factors on generation accuracy, such as the dimension, size of the training set and the considered number of neighbors K . We also provide a qualitative analysis that examines the factors affecting its accuracy. In addition, we explore the impact of SMOTE on classification boundary, and classification performance.
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Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
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Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
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A study of the behavior of several methods for balancing machine learning training data
Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007735 · Cited by 4,203
The class imbalance problem: A systematic study1
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Exploratory Undersampling for Class-Imbalance Learning
Liu, X.-Y., Wu, J., & Zhou, Z.-H. (2009) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2007853 · Cited by 2,465
Learning from class-imbalanced data: Review of methods and applications
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RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
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SMOTEBoost: Improving Prediction of the Minority Class in Boosting
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Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3045266 · Cited by 392
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Metadata
- Authors
- Elreedy, D., & Atiya, A. F.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 505
- Pages
- 32-64
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2963613787
- Cited by
- 756
- References
- 48
- FWCI
- 17.15
- Type
- journal article
- Language
- en
- Open access
- closed
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