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Sensor Systems and Software. 13th EAI International Conference, S-Cube 2022, Dalian, China, December 7-9, 2022, Proceedings

Research Article

CADM: Confusion Model-Based Detection Method for Real-Drift in Chunk Data Stream

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-34899-0_13,
        author={Songqiao Hu and Zeyi Liu and Xiao He},
        title={CADM: Confusion Model-Based Detection Method for Real-Drift in Chunk Data Stream},
        proceedings={Sensor Systems and Software. 13th EAI International Conference, S-Cube 2022, Dalian, China, December 7-9, 2022, Proceedings},
        proceedings_a={S-CUBE},
        year={2023},
        month={6},
        keywords={Concept drift Confusion model Chunk data stream Similarity},
        doi={10.1007/978-3-031-34899-0_13}
    }
    
  • Songqiao Hu
    Zeyi Liu
    Xiao He
    Year: 2023
    CADM: Confusion Model-Based Detection Method for Real-Drift in Chunk Data Stream
    S-CUBE
    Springer
    DOI: 10.1007/978-3-031-34899-0_13
Songqiao Hu1, Zeyi Liu2, Xiao He2,*
  • 1: School of Automation, Beijing Institute of Technology
  • 2: Department of Automation, Tsinghua University
*Contact email: hexiao@tsinghua.edu.cn

Abstract

Concept drift detection has attracted considerable attention due to its importance in many real-world applications such as health monitoring and fault diagnosis. Conventionally, most advanced approaches will be of poor performance when the evaluation criteria of the environment has changed (i.e. concept drift), either can only detect and adapt to virtual drift. In this paper, we propose a new approach to detect real-drift in the chunk data stream with limited annotations based on concept confusion. When a new data chunk arrives, we use both real labels and pseudo labels to update the model after prediction and drift detection. In this context, the model will be confused and yields prediction difference once drift occurs. We then adopt cosine similarity to measure the difference. And an adaptive threshold method is proposed to find the abnormal value. Experiments show that our method has a low false alarm rate and false negative rate with the utilization of different classifiers.

Keywords
Concept drift Confusion model Chunk data stream Similarity
Published
2023-06-10
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-34899-0_13
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