Adjusting Decision Boundary for Class Imbalanced Learning
The training of deep neural networks heavily depends on the data distribution. In particular, these networks easily suffer from class imbalance. The trained networks would recognize the frequent classes better than the infrequent classes. To resolve this problem, existing approaches typically propos...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9081988/ |