  Continual learning from data streams is among the most important topics in
contemporary machine learning. One of the biggest challenges in this domain
lies in creating algorithms that can continuously adapt to arriving data.
However, previously learned knowledge may become outdated, as streams evolve
over time. This phenomenon is known as concept drift and must be detected to
facilitate efficient adaptation of the learning model. While there exists a
plethora of drift detectors, all of them assume that we are dealing with
roughly balanced classes. In the case of imbalanced data streams, those
detectors will be biased towards the majority classes, ignoring changes
happening in the minority ones. Furthermore, class imbalance may evolve over
time and classes may change their roles (majority becoming minority and vice
versa). This is especially challenging in the multi-class setting, where
relationships among classes become complex. In this paper, we propose a
detailed taxonomy of challenges posed by concept drift in multi-class
imbalanced data streams, as well as a novel trainable concept drift detector
based on Restricted Boltzmann Machine. It is capable of monitoring multiple
classes at once and using reconstruction error to detect changes in each of
them independently. Our detector utilizes a skew-insensitive loss function that
allows it to handle multiple imbalanced distributions. Due to its trainable
nature, it is capable of following changes in a stream and evolving class
roles, as well as it can deal with local concept drift occurring in minority
classes. Extensive experimental study on multi-class drifting data streams,
enriched with a detailed analysis of the impact of local drifts and changing
imbalance ratios, confirms the high efficacy of our approach.
