Labelsets

Labelsets refer to the collection of labels used for annotating data in labeling tasks and to the specific set of labels assigned to an individual item. In practice, the term has two related meanings. First, the label vocabulary or labelset of a dataset is the predefined universe of labels that may be used to describe items. Second, a labelset for an item is the subset of that vocabulary that has been assigned to that item, which is especially common in multi-label annotation where a single item can belong to multiple categories.

Labelsets underpin supervised learning workflows. For multi-label classification, each data instance is associated with a labelset, and learning aims to predict the correct subset. The size and composition of a labelset influence model design and evaluation, with constraints sometimes imposed on the maximum number of labels per item or on co-occurrence patterns. Label vocabularies are often organized as hierarchies or ontologies, enabling semantic relationships such as parent-child and sibling labels.

Representation typically uses multi-label encodings such as multi-hot vectors or sparse sets. In data curation, constructing a robust labelset involves domain expertise, annotation guidelines, and quality control to mitigate label noise and imbalance.

Common applications span image and video tagging, text categorization, medical coding, and audio labeling. Evaluation can use per-label metrics (precision, recall, F1) and set-level metrics (subset accuracy, Hamming loss, Jaccard similarity).