Adaptive Information Fusion-Based Concept Drift Learning for Evolving Multiple Data Streams

Concept drift arises from unpredictable data distribution shifts, degrading model performance. In evolving multiple data streams, these drifts pose greater challenges due to dynamic changes and uncertain inter-stream correlations, demanding robust accuracy and generalization. To address this issue, in this article, we propose a novel multiple data stream learning method, called the adaptive information fusion-based concept drift learning (AIF-CD) method, to adaptively handle multiple data streams with heterogeneous feature spaces and complex drift situations. First, a real-time learning method with a cooperation scheme is proposed to handle multiple data streams. Second, an information fusion-based augmentation process is designed to help enhance the learning efficiency of each stream. Next, a drift severity identification-based adaptation strategy and a process to selectively use the previous timestamps' data are introduced to enhance learning robustness in both synchronous and asynchronous scenarios. Moreover, a detailed runtime complexity and theoretical analysis further explains the learning efficiency of our method. Our key innovation combines real-time adaptation with theoretical guarantees for complex, evolving multi-stream learning. The experiment results in various scenarios under synchronous and asynchronous settings show that the proposed method is more efficient than other benchmark methods.