Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus

2026-09-20

AI Summary

该研究提出了一种结合人类分析师的协作流式异常检测系统，通过集成异构检测器和基于归一化的加权共识来处理高速数据流中的异常检测问题。

Abstract

We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision loop. The system combines heterogeneous detectors and aggregates their outputs through a normalization-based weighted consensus, complemented by artifact-aware rules to stabilize anomaly scoring under deployment. To improve interpretability, it derives surrogate models that approximate the ensemble consensus and expose human-readable sensor conditions associated with anomalous behavior. Analysts can actively intervene by reviewing anomaly episodes, adjusting consensus behavior, and refining surrogate rules used for anomaly prediction, producing a human-adjusted ensemble. We evaluate the approach on an industrial stream with 260\,000 events and 3 anomalous episodes, showing robust detection and actionable human-AI interaction.

Problem

• anomaly detection
• data streams
• human analysts
• ensemble consensus
• interpretability

Innovation

• collaborative streaming anomaly detection
• interactive explanations
• ensemble consensus
• human-adjusted ensemble
• surrogate models