  We have witnessed in recent years an ever-growing volume of information
becoming available in a streaming manner in various application areas. As a
result, there is an emerging need for online learning methods that train
predictive models on-the-fly. A series of open challenges, however, hinder
their deployment in practice. These are, learning as data arrive in real-time
one-by-one, learning from data with limited ground truth information, learning
from nonstationary data, and learning from severely imbalanced data, while
occupying a limited amount of memory for data storage. We propose the
ActiSiamese algorithm, which addresses these challenges by combining online
active learning, siamese networks, and a multi-queue memory. It develops a new
density-based active learning strategy which considers similarity in the latent
(rather than the input) space. We conduct an extensive study that compares the
role of different active learning budgets and strategies, the performance
with/without memory, the performance with/without ensembling, in both synthetic
and real-world datasets, under different data nonstationarity characteristics
and class imbalance levels. ActiSiamese outperforms baseline and
state-of-the-art algorithms, and is effective under severe imbalance, even only
when a fraction of the arriving instances' labels is available. We publicly
release our code to the community.
