Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (12), pp. 12828-12841
- Issue Date:
- 2023-12-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network.pdf | Accepted version | 3.27 MB |
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Concept drift is the phenomenon where the data distribution in a data stream changes over time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause a jam in a certain period, leading to a distribution change in traffic speed. Most research in the concept drift field focuses on single data stream, however, few of them consider multi-stream environments which are more in line with the application needs. To fill this gap, we propose a multi-stream prediction setting and a multi-stream concept drift self-adaptation framework using graph neural network, named SAGN. In SAGN, we reconsider the learning procedure of GNN-based predictors from an aspect of concept drift adaptation for multi-stream. By this design, the prediction task is converted into online streaming data tasks in sub-graphs. Each sub-graph corresponds to an adaptation target and will be updated over time. In this way, locally we can overcome drift in each sub-graph by a designed adaptation technique, and globally the correlation between different data streams is well-preserved as a graph structure. Therefore, whether drift occurs or not, in one or several streams, SAGN can provide consistently accurate prediction results. We comprehensively tested SAGN on both synthetic and real-world, drift and non-drift data in the multi-step prediction task. The experiment results show that SAGN is able to achieve state-of-the-art performance in most cases.
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