Concept Drift Detection Based on Deep Neural Networks and Autoencoders - Lisha Hu; Yaru Lu; Yuehua Feng (Applied Sciences)
In domains such as fraud detection, healthcare, and industrial equipment maintenance, streaming data often exhibit characteristics such as continuous generation, high real-time processing requirements, and complex distributions, making it susceptible to concept drift. Traditional shallow models, with their limited representational capacity, struggle to fully capture the latent conceptual knowledge inherent in the dynamic and evolving nature of streaming data. To address this challenge, we propose a concept drift detection method based on deep neural networks combined with autoencoders (Concept Drift Detection Based on Deep Neural Network Combined with Autoencoder, DNN+AE-DD). In the DNN+AE-DD, a deep neural network is first employed as the base model for pretraining, and the hidden layer parameters of the model are transferred to a network with an identical structure for stream data processing, where certain hidden layers are frozen. 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<semantics>σ</semantics> principle. Experimental results on both real-world and synthetic datasets demonstrate that, compared to traditional shallow concept drift detection methods, this approach effectively and accurately detects anomalies in streaming data, confirming the proposed model’s high sensitivity to concept drift.