Autonomous learning for multiple data streams under concept drift
- Publication Type:
- Thesis
- Issue Date:
- 2025
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With the increasing prevalence of streaming data in real-world applications, addressing concept drift—unpredictable changes in data distributions—has become a crucial challenge for maintaining model accuracy. While traditional machine learning models assume stationary data streams, real-world streams frequently exhibit non-stationarity and concept drift—unpredictable changes in data distribution that undermine the effectiveness of conventional algorithms. Most existing studies focus on handling drift in single data streams, whereas real-world scenarios often involve multiple, interdependent streams where isolated modeling neglects important cross-stream correlations. This thesis proposes a series of effective frameworks for Autonomous Learning for Multiple Data Streams under Concept Drift (ALMCD). Our approach explicitly incorporates the dynamic relationships among multiple data streams, enabling the model to learn and adapt to both individual and joint distribution changes. By dynamically capturing inter-stream structural changes, our frameworks can automatically detect and adapt to various types of concept drift. Experimental results demonstrate that our methods significantly improve real-time prediction accuracy and adaptability compared to traditional single-stream approaches. These findings provide a practical solution for robust data mining in complex, non-stationary environments such as transportation networks and weather prediction systems.
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