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

Kun Wang, Jie Lu, Anjin Liu

Abstract: 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.

External IDs:dblp:journals/tkde/WangLL25a