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


  • Chao Chen Nanjing University
  • Dawei Wang Alibaba Group
  • Feng Mao Alibaba Group
  • Zongzhang Zhang Nanjing University
  • Yang Yu Nanjing University



Reinforcement Learning, Deep Learning, Anomaly Detection


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.




How to Cite

Chen, C., Wang, D., Mao, F., Zhang, Z., & Yu, Y. (2023). Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16180-16181.