  An enormous and ever-growing volume of data is nowadays becoming available in
a sequential fashion in various real-world applications. Learning in
nonstationary environments constitutes a major challenge, and this problem
becomes orders of magnitude more complex in the presence of class imbalance. We
provide new insights into learning from nonstationary and imbalanced data in
online learning, a largely unexplored area. We propose the novel Adaptive
REBAlancing (AREBA) algorithm that selectively includes in the training set a
subset of the majority and minority examples that appeared so far, while at its
heart lies an adaptive mechanism to continually maintain the class balance
between the selected examples. We compare AREBA with strong baselines and other
state-of-the-art algorithms and perform extensive experimental work in
scenarios with various class imbalance rates and different concept drift types
on both synthetic and real-world data. AREBA significantly outperforms the rest
with respect to both learning speed and learning quality. Our code is made
publicly available to the scientific community.
