Tree-based Self-adaptive Anomaly Detection by Human-Machine Interaction

Anomaly detectors are used to distinguish the difference between normal and abnormal data, which are usually implemented by evaluating and ranking anomaly scores of each instance. Static unsupervised anomaly detectors can be difficult to adjust anomaly score calculation for streaming data. In real scenarios, anomaly detection often needs to be regulated by human feedback, which benefits to adjust anomaly detectors. In this paper, we propose a human-machine interactive anomaly detection method, named ISPForest, which can be adaptively updated under the guidance of human feedback. In particular, the feedback will be used to adjust the anomaly score calculation and structure of the tree-based detector, ideally attaining more accurate anomaly scores in the future. Our main contribution is to improve the tree model that can be dynamically updated from perspectives of anomaly score calculation and the model's structure. Our approach is instantiated for the powerful class of tree-based anomaly detectors, and we conduct experiments on a range of benchmark datasets. The results demonstrate that human expert feedback is helpful to improve the accuracy of anomaly detectors.