Hybrid Ensemble Framework for Imbalanced Data Streams With Concept Drift

In the field of online learning, handling imbalanced data streams with concept drift has become a substantial challenge. Research interest in the combined challenges of concept drift and class imbalance is steadily growing. When dealing with concept drift, existing methods usually adopt active detection or passive adaptation to deal with concept drift. However, a single adaptation method is relatively limited in dealing with joint concept drift. This paper introduces a novel hybrid ensemble framework (HEF-IDS) specifically designed to tackle drifting data streams while effectively accommodating class imbalance. The framework integrates an active detection module and a passive adaptation module. In the passive adaptation module, we use a hybrid sampling strategy to balance data chunks, construct a sub-ensemble classifier for each balanced chunk, and combine it with a weighted voting scheme to deal with the class imbalance and adapt to the latest concept. In the process of active detection, we design a new sample selection mechanism that stores the most informative samples in the buffer, and uses these samples for training when drift is detected to prevent catastrophic forgetting. Comparative results on artificial and real-world datasets show that the proposed HEF-IDS is superior to other advanced online classification methods.