GMO-AC: Gaussian-Based Minority Oversampling With Adaptive Outlier Filtering and Class Overlap Weighting
Imbalanced data significantly affects the performance of standard classification models. Data-level approaches primarily use oversampling methods, such as the synthetic minority oversampling technique (SMOTE), to address this problem. However, because methods such as SMOTE generate instances via lin...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10804168/ |
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