  An ever increasing volume of data is nowadays becoming available in a
streaming manner in many application areas, such as, in critical infrastructure
systems, finance and banking, security and crime and web analytics. To meet
this new demand, predictive models need to be built online where learning
occurs on-the-fly. Online learning poses important challenges that affect the
deployment of online classification systems to real-life problems. In this
paper we investigate learning from limited labelled, nonstationary and
imbalanced data in online classification. We propose a learning method that
synergistically combines siamese neural networks and active learning. The
proposed method uses a multi-sliding window approach to store data, and
maintains separate and balanced queues for each class. Our study shows that the
proposed method is robust to data nonstationarity and imbalance, and
significantly outperforms baselines and state-of-the-art algorithms in terms of
both learning speed and performance. Importantly, it is effective even when
only 1% of the labels of the arriving instances are available.
