Relabel

Relabel is a verb meaning to assign a different label to an item or to change the label that has been attached to something. In data contexts, relabeling is the process of replacing one label with another, often to reflect updated taxonomy, corrected classifications, or renamed categories.

In data labeling and annotation, relabeling occurs when labels are found to be inaccurate, or when the labeling scheme evolves. It can involve merging categories, splitting a category into finer classes, or reclassifying individual samples. Relabeling is commonly performed manually, but automated approaches such as active learning, label propagation, or crowdsourced quality control may also be used, with version tracking to preserve a history of changes.

In machine learning and data processing, relabeling can address label noise or distribution shifts. Correcting labels can improve model training and evaluation, while excessive relabeling risks introducing bias if the changes are not well justified. Semi-supervised and weakly supervised methods may rely on relabeled data to propagate more accurate supervision signals.

In statistics and related mathematics, relabeling often refers to changing the identifiers of categorical components, such as permuting class labels. This arises in problems with symmetric solutions, notably in the label switching phenomenon in Bayesian mixture models. Techniques to resolve relabeling ambiguities include imposing identifiability constraints or applying post hoc relabeling algorithms.

In databases and information management, relabeling can mean renaming a field, attribute, or taxonomy term within a dataset or ontology. Such changes require mapping rules and careful data migration to maintain compatibility with existing queries and applications.