Deep Anomaly Detection and Search via Reinforcement Learning
This addresses a bottleneck in semi-supervised anomaly detection for data mining applications, but it is incremental as it builds on existing methods with a hybrid approach.
The paper tackles the problem of insufficient exploitation of labeled data and under-exploration of unlabeled data in semi-supervised anomaly detection by proposing Deep Anomaly Detection and Search (DADS), which uses reinforcement learning to balance exploitation and exploration, achieving good performance in detecting both known and unknown anomalies.
Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers. In this paper, we classify existing semi-supervised AD methods into two categories: unsupervised-based and supervised-based, and point out that most of them suffer from insufficient exploitation of labeled data and under-exploration of unlabeled data. To tackle these problems, we propose Deep Anomaly Detection and Search (DADS), which applies Reinforcement Learning (RL) to balance exploitation and exploration. During the training process, the agent searches for possible anomalies with hierarchically-structured datasets and uses the searched anomalies to enhance performance, which in essence draws lessons from the idea of ensemble learning. Experimentally, we compare DADS with several state-of-the-art methods in the settings of leveraging labeled known anomalies to detect both other known anomalies and unknown anomalies. Results show that DADS can efficiently and precisely search anomalies from unlabeled data and learn from them, thus achieving good performance.