Deep Anomaly Detection and Search via Reinforcement Learning (Student 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.

Authors

• Chao Chen Harbin Institute of Technology
• Dawei Wang
• Feng Mao Alibaba Group
• Zongzhang Zhang National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China
• Yang Yu School of Artificial Intelligence, Jilin University, Changchun Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University, Changchun

Keywords

• Anomaly Detection
• Deep Learning
• Reinforcement Learning