Anticipating to Change: A Proactive Approach for Concept Drift Adaptation in Data Streams

Abstract Adapting to drifting data streams remains a key challenge in online learning, where effective model adaptation depends on timely concept drift detection. Most existing approaches respond to drift only after distributional changes occur, reacting to concept drift, limiting their ability to prevent the classifier’s performance degradation. This work introduces a novel methodology to anticipate concept drift and enable proactive adaptation before the data distribution shift negatively impacts the classifier. We propose four proactive adaptation strategies based on the Very Fast Decision Tree (VFDT) algorithm to leverage data trends to estimate proactive changes in the classifier, mitigating or even preventing the performance degradation consequence of the concept drift. We evaluate the proposed methods across four scenarios with diverse data stream configurations. Results demonstrate that proactive adaptation reduces the adverse effects of concept drift and improves classification performance. In particular, the proposed strategies consistently outperformed in settings with incremental drift, underscoring the potential of anticipatory approaches and addressing a notable gap in the current literature.