  Online class imbalance learning constitutes a new problem and an emerging
research topic that focusses on the challenges of online learning under class
imbalance and concept drift. Class imbalance deals with data streams that have
very skewed distributions while concept drift deals with changes in the class
imbalance status. Little work exists that addresses these challenges and in
this paper we introduce queue-based resampling, a novel algorithm that
successfully addresses the co-existence of class imbalance and concept drift.
The central idea of the proposed resampling algorithm is to selectively include
in the training set a subset of the examples that appeared in the past. Results
on two popular benchmark datasets demonstrate the effectiveness of queue-based
resampling over state-of-the-art methods in terms of learning speed and
quality.
