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Although the issues of concept drift and class imbalance have been studied separately, the joint problem is underexplored even though it has received increasing attention. Concept drift is further complicated when the dataset is class imbalanced. Meanwhile, most of the existing techniques have ignored the influence of complex data distribution on learning imbalanced data streams.

To overcome these issues, we propose an ensemble-based model for learning concept drift from imbalanced data streams with complex data distribution, called selection-based resampling ensemble (SRE). SRE combines the operators of resampling and periodical update to handle the joint issue. In the chunk-based framework, a selection-based resampling mechanism, which focuses on drifting and unsafe examples, is first employed to re-balance the class distribution of the latest block. Then, previous ensemble members are …