labelset

A label set is a collection of labels used to categorize items in data labeling, tagging, and classification systems. It defines the vocabulary that annotators can assign to an item and helps enforce consistency across a project.

Label sets may be flat or hierarchical; a taxonomy or ontology can be used to organize labels, provide synonyms, and establish relationships. In many systems a label set is maintained as a predefined list that users select from when annotating.

In machine learning, label sets are central to multi-label classification, where an item may be assigned multiple labels from the set. They also guide evaluation and dataset construction. In content management, label sets power search and filtering and enable standardized tagging across large corpora or media collections.

Managing a label set includes versioning, governance, deduplication, and handling drift as domains evolve. Synonym mapping and hierarchical relations help maintain consistency as new labels are added.

A labeled dataset associates each item with a subset of the label set. Model evaluation compares predicted label sets to reference label sets using metrics such as precision, recall, and F1 for multi-label tasks.

Example: a photo dataset may use a label set {outdoors, person, vehicle, portrait, nighttime}. A given image might be annotated with {outdoors, person, nighttime}.

See also: taxonomy, ontology, tagging, multi-label classification, metadata.