The Value of Diversity for Dealing with Concept Drift in Class-Imbalanced Data Streams

Concept drift and class imbalance are critical chal-lenges in real-time data stream learning. Existing ensemble methods use homogeneous diversity (models for the same con-cept) to tackle these challenges but often overlook heterogeneous diversity (models from different concepts), which could improve adaptation, especially with scarce minority data. This paper provides the first analysis of when and why each type of diversity is beneficial for class-imbalanced data streams. To enable this analysis, we introduce CDCMS.CIL, a novel class imbalance learning framework for leveraging heterogeneous diversity. Experiments based on 80 artificial and 9 real-world data streams show that heterogeneous diversity can significantly aid concept drift handling in highly imbalanced scenarios, while homogeneous diversity is better during stable periods. These findings provide crucial guidance for designing robust ensembles for drifting class imbalanced data streams.