The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
Open accessAbstract
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages - from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.
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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
Self-citationLearning from imbalanced data: open challenges and future directions
Krawczyk, B. (2016) · Progress in Artificial Intelligence · doi:10.1007/s13748-016-0094-0 · Cited by 2,459
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310
Self-citationRamírez-Gallego, S., Fernández, A., García, S., Chen, M., & Herrera, F. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.001 · Cited by 140
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Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019) · 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) · doi:10.1109/infoteh.2019.8717766 · Cited by 323
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Metadata
- Authors
- Fernandez, A., Garcia, S., Herrera, F., & Chawla, N. V.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2018-04-20
- Volume
- 61
- Pages
- 863-905
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2800788706
- Cited by
- 2,219
- References
- 4
- FWCI
- 64.50
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Fernandez, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.1.11192
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