Dynamic Graph Regularization for Multi-Stream Concept Drift Self-adaptation
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Knowledge and Data Engineering, 2024, PP, (99), pp. 1-13
- Issue Date:
- 2024-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1728040.pdf | Published version | 2.64 MB |
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Concept drift is an inevitable problem in non-stationary data stream environments, due to changes in data distribution over time. In practical applications, multi-stream data is more common and complex than single-stream data, yet they have received little attention. Addressing the concept drift problem while mining correlations between data streams has become a significant challenge. Several research works focus on capturing the correlation between streams using graph neural networks (GNNs), which provides valuable insights. However, these methods fix the correlation graph structure after training and are unable to adapt to the new data distribution with dynamic correlations during testing. To bridge this gap, we propose a novel concept drift self-adaptation framework based on dynamic graph regularization for multi-stream, named Multi-stream Self-adaptation based on Graph Regularization (MSGR). A new graph neural network architecture is proposed to capture deep spatio-temporal correlations and learn a correlation graph structure without any pre-defined graphs. Each node on the graph represents a stream. The correlation graph structure is constructed through Gumbel sampling and an adaptive matrix from the perspective of stream pairs. Thus we attain a high-performance GNN as the base prediction model for the multi-stream multi-step prediction task in the testing stage. To adapt to the new data distribution, we design a self-adaptation mechanism performed by assigning dynamic learning weight for newly arriving samples. Intuitively, we should assign larger learning weights for relevant samples when drift occurs. The self-adaptation process is accomplished by the sub-graph updating and the proposed graph regularization. Error-based drift detection is integrated into the framework. When drift is detected, the weight for sub-graph updating is increased by adjusting the regularization coefficient. In this way, regardless of the type and degree of concept drift occurring on one or more streams, MSGR can achieve high self-adaptation performance and provide accurate prediction results consistently. The comprehensive testing results on both real-world and synthetic datasets show that MSGR can achieve state-of-the-art performance.
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