An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift.
A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection has been well studied in recent years. However, most of the researches assume that the class of data st...
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| Автори: | , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Public Library of Science (PLoS)
2024-01-01
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| Серія: | PLoS ONE |
| Онлайн доступ: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292140&type=printable |
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