# Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus

arXiv: https://arxiv.org/abs/2609.23883 · HTML: https://mlsanity.com/paper/2609.23883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 20 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [LSCP: Locally Selective Combination in Parallel Outlier Ensembles](https://mlsanity.com/paper/1812.01528/) (2018)
- [Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?](https://mlsanity.com/paper/2412.14232/) (2024)
- [Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection](https://mlsanity.com/paper/2210.10487/) (2022)
- [DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices](https://mlsanity.com/paper/2502.14011/) (2025)
- [In-context Learning of Evolving Data Streams with Tabular Foundational Models](https://mlsanity.com/paper/2502.16840/) (2025)
- [On-device edge learning for IoT data streams: a survey](https://mlsanity.com/paper/2502.17788/) (2025)
- [Bridging Streaming Continual Learning via In-Context Large Tabular Models](https://mlsanity.com/paper/2512.11668/) (2025)
- [Online hierarchical partitioning of the output space in extreme multi-label data stream](https://mlsanity.com/paper/2507.20894/) (2025)
- [Explainable Anomaly Detection for Industrial IoT Data Streams](https://mlsanity.com/paper/2512.08885/) (2025)
- [Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations](https://mlsanity.com/paper/2504.10151/) (2025)
- [Boosting-inspired online learning with transfer for railway maintenance](https://mlsanity.com/paper/2504.08554/) (2025)
- [Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles](https://mlsanity.com/paper/2605.12803/) (2026)
- [Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring](https://mlsanity.com/paper/2602.16101/) (2026)
- [Streaming Hierarchical Inference with Tabular Foundation Models](https://mlsanity.com/paper/2609.07956/) (2026)

## Abstract (arXiv)

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.