  Unlabelled data appear in many domains and are particularly relevant to
streaming applications, where even though data is abundant, labelled data is
rare. To address the learning problems associated with such data, one can
ignore the unlabelled data and focus only on the labelled data (supervised
learning); use the labelled data and attempt to leverage the unlabelled data
(semi-supervised learning); or assume some labels will be available on request
(active learning). The first approach is the simplest, yet the amount of
labelled data available will limit the predictive performance. The second
relies on finding and exploiting the underlying characteristics of the data
distribution. The third depends on an external agent to provide the required
labels in a timely fashion. This survey pays special attention to methods that
leverage unlabelled data in a semi-supervised setting. We also discuss the
delayed labelling issue, which impacts both fully supervised and
semi-supervised methods. We propose a unified problem setting, discuss the
learning guarantees and existing methods, explain the differences between
related problem settings. Finally, we review the current benchmarking practices
and propose adaptations to enhance them.
