TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation
提出一种基于时间分割的数据流学习方法TS-DM，通过分块分割策略处理正常和漂移数据块，并引入警告级分割和高低漂移权衡机制，提升模型在概念漂移环境下的泛化性和鲁棒性。
Concept drift arises from the uncertainty of data distribution over time and is common in data stream. While numerous methods have been developed to assist machine learning models in adapting to such changeable data, the problem of improperly keeping or discarding data samples remains. This may results in the loss of valuable knowledge that could be utilized in subsequent time points, ultimately affecting the model's accuracy. To address this issue, a novel method called time segmentation-based data stream learning method (TS-DM) is developed to help segment and learn the streaming data for concept drift adaptation. First, a chunk-based segmentation strategy is given to segment normal and drift chunks. Building upon this, a chunk-based evolving segmentation (CES) strategy is proposed to mine and segment the data chunk when both old and new concepts coexist. Furthermore, a warning level data segmentation process (CES-W) and a high-low-drift tradeoff handling process are developed to enhance the generalization and robustness. To evaluate the performance and efficiency of our proposed method, we conduct experiments on both synthetic and real-world datasets. By comparing the results with several state-of-the-art data stream learning methods, the experimental findings demonstrate the efficiency of the proposed method.