Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

@article{Zha2020MetaAADAA,
  title={Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning},
  author={D. Zha and Kwei-Herng Lai and Mingyang Wan and X. Hu},
  journal={2020 IEEE International Conference on Data Mining (ICDM)},
  year={2020},
  pages={771-780}
}
  • D. Zha, Kwei-Herng Lai, +1 author X. Hu
  • Published 2020
  • Computer Science, Mathematics
  • 2020 IEEE International Conference on Data Mining (ICDM)
High false-positive rate is a long-standing challenge for anomaly detection algorithms, especially in high-stake applications. To identify the true anomalies, in practice, analysts or domain experts will be employed to investigate the top instances one by one in a ranked list of anomalies identified by an anomaly detection system. This verification procedure generates informative labels that can be leveraged to re-rank the anomalies so as to help the analyst to discover more true anomalies… Expand
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