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Unlabelled data appear in many domains and are particularly relevant tostreaming applications, where even though data is abundant, labelled data israre. To address the learning problems associated with such data, one canignore the unlabelled data and focus only on the labelled data (supervisedlearning); 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 oflabelled data available will limit the predictive performance. The secondrelies on finding and exploiting the underlying characteristics of the datadistribution. The third depends on an external agent to provide the requiredlabels in a timely fashion. This survey pays special attention to methods thatleverage unlabelled data in a semi-supervised setting. We also discuss thedelayed labelling issue, which impacts both fully supervised andsemi-supervised methods. We propose a unified problem setting, discuss thelearning guarantees and existing methods, explain the differences betweenrelated problem settings. Finally, we review the current benchmarking practicesand propose adaptations to enhance them.

A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams