  As an emerging research topic, online class imbalance learning often combines
the challenges of both class imbalance and concept drift. It deals with data
streams having very skewed class distributions, where concept drift may occur.
It has recently received increased research attention; however, very little
work addresses the combined problem where both class imbalance and concept
drift coexist. As the first systematic study of handling concept drift in
class-imbalanced data streams, this paper first provides a comprehensive review
of current research progress in this field, including current research focuses
and open challenges. Then, an in-depth experimental study is performed, with
the goal of understanding how to best overcome concept drift in online learning
with class imbalance. Based on the analysis, a general guideline is proposed
for the development of an effective algorithm.
