An Ensemble-Based Semi-Supervised Learning Approach for Non-Stationary Imbalanced Data Streams with Label Scarcity

Abstract

Addressing the challenges of learning from multi-class imbalanced data streams, particularly in scenarios with scarce labeled data and concept drift, remains an open problem in the field of data stream mining. Despite the prevalence of such data scenarios in real-world applications, existing approaches have yet to provide effective solutions. In this paper, we propose a novel chunk-based semi-supervised framework, GMCSSEL, which leverages an ensemble of base classifiers trained on micro-cluster centers with labels inferred through a graph-fusion and label propagation process. Our approach incorporates chunk-based incremental label propagation by integrating both current and previously inferred label information into the propagation equation, with regularization parameters applied to control their influence. Furthermore, our method includes a novel concept drift detection mechanism specifically designed for imbalanced data with label scarcity. The imbalanced data problem is addressed through a combination of graph fusion, label matrix normalization, and SMOTE techniques. Experimental results on synthetic data streams with varying class ratios and concept drifts, as well as real multi-class streams, demonstrate the superior classification performance of our approach compared to the IOE semi-supervised algorithm. Our method achieves an average increase in evaluation metrics of 12.5% across multiple data streams, with improvements ranging from 8% to 18% in G-mean, AUC, Kappa, and F1-score metrics. Statistical analysis confirms that these improvements are significant, highlighting the robustness of our approach in handling non-stationary imbalanced data streams with label scarcity.