Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract)

Chao Chen, Dawei Wang, Feng Mao, Zongzhang Zhang, Yang Yu

AAAI 2023 pp. 16180-16181

doi:10.1609/AAAI.V37I13.26950 /aaai/2023/chen2023aaai-deep/

Abstract

Semi-supervised anomaly detection is a data mining task which aims at learning features from partially-labeled datasets. We propose Deep Anomaly Detection and Search (DADS) with reinforcement learning. During the training process, the agent searches for possible anomalies in unlabeled dataset to enhance performance. Empirically, we compare DADS with several methods in the settings of leveraging known anomalies to detect both other known and unknown anomalies. Results show that DADS achieves good performance.

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Cite

Text

Chen et al. "Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26950

Markdown

[Chen et al. "Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/chen2023aaai-deep/) doi:10.1609/AAAI.V37I13.26950

BibTeX

@inproceedings{chen2023aaai-deep,
  title     = {{Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract)}},
  author    = {Chen, Chao and Wang, Dawei and Mao, Feng and Zhang, Zongzhang and Yu, Yang},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16180-16181},
  doi       = {10.1609/AAAI.V37I13.26950},
  url       = {https://mlanthology.org/aaai/2023/chen2023aaai-deep/}
}