Summary of Semi-supervised Anomaly Detection Via Adaptive Reinforcement Learning-enabled Method with Causal Inference For Sensor Signals, by Xiangwei Chen et al.
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals
by Xiangwei Chen, Ruliang Xiaoa, Zhixia Zeng, Zhipeng Qiu, Shi Zhang, Xin Du
First submitted to arxiv on: 11 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Triple-Assisted Causal Reinforcement Learning Anomaly Detector (Tri-CRLAD) is a novel approach to semi-supervised anomaly detection for sensor signals in smart manufacturing. The model leverages causal inference to extract intrinsic causal features, improving its utilization of prior knowledge and generalization capability. Tri-CRLAD also features a triple decision support mechanism, including sampling based on historical similarity, adaptive threshold smoothing adjustment, and an adaptive decision reward mechanism. Experimental results across seven diverse datasets demonstrate that Tri-CRLAD outperforms nine state-of-the-art baseline methods, achieving up to 23% improvement in anomaly detection stability with minimal known anomaly samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tri-CRLAD is a new way to find problems in sensor data from smart factories. It’s like having a superpower that helps machines work better and avoid mistakes. The model uses special math to figure out what’s important in the data, which makes it more accurate and reliable. This means that Tri-CRLAD can detect unusual patterns or changes in the data even when there isn’t much training data available. |
Keywords
» Artificial intelligence » Anomaly detection » Generalization » Inference » Reinforcement learning » Semi supervised
