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

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Sensor Systems and Software (S-Cube 2022)


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.

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This work was supported by National Natural Science Foundation of China under Grant 61733009, National Key Research and Development Program of China under Grant 2017YFA0700300, and Huaneng Group science and technology research project.

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Correspondence to Xiao He .

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Hu, S., Liu, Z., He, X. (2023). CADM: Confusion Model-Based Detection Method for Real-Drift in Chunk Data Stream. In: Karimi , H.R., Wang, N. (eds) Sensor Systems and Software. S-Cube 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 487. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34898-3

  • Online ISBN: 978-3-031-34899-0

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