  Online class imbalance learning deals with data streams that are affected by
both concept drift and class imbalance. Online learning tries to find a
trade-off between exploiting previously learned information and incorporating
new information into the model. This requires both the incremental update of
the model and the ability to unlearn outdated information. The improper use of
unlearning, however, can lead to the retroactive interference problem, a
phenomenon that occurs when newly learned information interferes with the old
information and impedes the recall of previously learned information. The
problem becomes more severe when the classes are not equally represented,
resulting in the removal of minority information from the model. In this work,
we propose the Drift-Aware Multi-Memory Model (DAM3), which addresses the class
imbalance problem in online learning for memory-based models. DAM3 mitigates
class imbalance by incorporating an imbalance-sensitive drift detector,
preserving a balanced representation of classes in the model, and resolving
retroactive interference using a working memory that prevents the forgetting of
old information. We show through experiments on real-world and synthetic
datasets that the proposed method mitigates class imbalance and outperforms the
state-of-the-art methods.
