The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions
Abstract
In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.
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Cites
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Interpretable machine learning for imbalanced credit scoring datasets
Chen, Y., Calabrese, R., & Martin-Barragan, B. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.036 · Cited by 190
Deng, Y., Lu, L., Aponte, L., Angelidi, A. M., Novak, V., Karniadakis, G. E., & Mantzoros, C. S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00480-x · Cited by 152
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Wu, B., Liu, L., Yang, Y., Zheng, K., & Wang, X. (2020) · IEEE Access · doi:10.1109/access.2020.2975630 · Cited by 54
Alalwany, E., & Mahgoub, I. (2022) · Sensors · doi:10.3390/s22239195 · Cited by 36
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Metadata
- Authors
- Kaur, H., Pannu, H. S., & Malhi, A. K.
- Venue
- ACM Computing Surveys
- Published
- 2019-08-30
- Volume
- 52
- Issue
- 4
- Pages
- 1-36
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2971644666
- Cited by
- 488
- References
- 116
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
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