  In our digital universe nowadays, enormous amount of data are produced in a
streaming manner in a variety of application areas. These data are often
unlabelled. In this case, identifying infrequent events, such as anomalies,
poses a great challenge. This problem becomes even more difficult in
non-stationary environments, which can cause deterioration of the predictive
performance of a model. To address the above challenges, the paper proposes an
autoencoder-based incremental learning method with drift detection
(strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of
both incremental learning and drift detection. We conduct an experimental study
using real-world and synthetic datasets with severe or extreme class imbalance,
and provide an empirical analysis of strAEm++DD. We further conduct a
comparative study, showing that the proposed method significantly outperforms
existing baseline and advanced methods.
