Dynamic Chunk-Based Active Learning Based on Enhanced Broad Learning System for Imbalanced Drifting Data Streams

Mianfen Lin, Zhiwen Yu, Kaixiang Yang, C. L. Philip Chen

Abstract: The processing of continuous data streams in non-stationary environments has gained increasing attention. However, supervised online learning is often limited by label availability. Furthermore, it is crucial to develop a stable and high-performance online method in non-stationary environments. To tackle these issues, we propose a dynamic chunk-based active learning framework (DCAL). This framework includes a dynamic dual-stage query strategy and an enhanced active learning model. Specifically, the proposed query strategy, referred to as DyDQS, evaluates sample value comprehensively by considering local density, uncertainty, and dynamic imbalance ratio. This approach selects samples that are both representative and uncertain, while also enhancing the likelihood of selecting minority class samples. Additionally, we introduce an enhanced active learning model, named eBLS-W, which is based on the broad learning system (BLS). We redesign the update rule of BLS and equip it with a kernel mapping to improve its robustness and performance, enabling it to better handle non-stationary environments. The effectiveness of the DyDQS, eBLS-W, and DCAL was validated through experiments on synthetic datasets with drift and real-world datasets. The results demonstrate that our approach outperforms other advanced methods in terms of robustness and accuracy.

External IDs:doi:10.1109/tkde.2025.3631126