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    Concept Drift Detection Based on Deep Neural Networks and Autoencoders by Lisha Hu, Yaru Lu, Yuehua Feng

    Published 2025-03-01
    “…Subsequently, the hidden layer outputs from both the pretraining and stream data processing phases are collected and used as training and testing data to initialize and predict using an autoencoder model. Concept drift is then detected by combining the reconstruction error of the autoencoder with the 3<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula> principle. …”
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