Resampling approaches to handle class imbalance: a review from a data perspective
Abstract This article presents a data-driven review of resampling approaches aimed at mitigating the class imbalance problem in machine learning, a widespread issue that limits classifier performance across numerous sectors. Initially, this research provides an extensive theoretical examination of t...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-03-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01119-4 |
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