Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

Understand

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 given a time budget.
• Some re-ranking strategies have been proposed to approximate the above sequential decision process.