# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 5 Conclusions and Future Work  
To address concept drift in online scenarios such as fraud detection, healthcare, and industrial equipment maintenance, this paper proposes a concept drift detection method based on deep neural networks and autoencoders (DNN+AE-DD). The method employs a deep neural network as the base classifier, initially trained on static data. After training, the hidden layer outputs are collected to generate training data for the autoencoder. During testing, the autoencoder processes hidden layer outputs from streaming data. Once trained (initialized), the 3\(\sigma\) principle is applied to determine whether drift has occurred. The proposed method is compared with several classical drift detection techniques, demonstrating its timeliness and effectiveness in handling various types of drifts, including single and multiple drifts. The results indicate that the method enhances drift detection sensitivity, ensuring robust performance across different datasets. Despite the proposed algorithm demonstrating significant advantages in detection delay and other aspects, it still has certain limitations in the following areas:  
* The computational complexity of the DNN+AE-DD algorithm is relatively high, primarily due to the iterations and backpropagation during the training process. In contrast, other algorithms are not constrained by this bottleneck. Future work will focus on optimizing the efficiency of the training process to mitigate this challenge. * This study focuses solely on experiments with traditional feedforward neural networks and does not include models widely used in the current deep learning field, such as deep recurrent neural networks. Furthermore, due to the relatively simple model structure, techniques such as model compression are not addressed in this study. Compared to feedforward neural networks, recurrent neural networks can more effectively capture long-term and short-term dependencies in data and have the ability to retain historical information. Therefore, they may have significant potential advantages in addressing recurring concept drift. In the future, we will conduct experiments using large models on ultra-large-scale datasets and perform in-depth research by integrating different types of networks and transfer learning. Additionally, we will design detailed optimization techniques such as knowledge distillation and model compression, aiming to discover and develop more effective detection algorithms. * This algorithm is specifically designed for detecting concept drift. In related work, we discussed that current approaches to addressing concept drift problems primarily fall into two categories: drift detection and model adaptation. Integrating drift detection with model adaptation can accelerate a model's adaptation to new data distributions while enhancing its generalization ability. In future work, we plan to leverage the detection outputs of this algorithm for model adaptation, aiming to improve the model's robustness and adaptability in handling concept drift. * This approach assumes an idealized scenario where the model operates without missing data, noisy data, adversarial data, or feature evolution. However, these challenges are more prevalent and hold greater practical significance in real-world streaming data applications. Addressing missing values, enhancing robustness to noisy data,improving resistance to adversarial data, and designing models to accommodate feature evolution are crucial research directions. In future work, we aim to develop more comprehensive solutions to tackle these complex data scenarios. Conceptualization, L.H. ; methodology, L.H. and Y.L. ; software, Y.L. ; validation, L.H., Y.L. and Y.F. ; formal analysis, Y.F. ; investigation, Y.F. ; resources, L.H. ; data curation, Y.L. ; writing--original draft preparation, Y.L. ; writing--review and editing, Y.L. ; visualization, Y.L. ; supervision, L.H. ; project administration, L.H. ; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.