relabeling

Relabeling is the process of changing labels attached to items, records, categories, or components. It is performed to correct errors, accommodate updated terminology, or align with a reference standard. Relabeling is used across fields where labels serve to distinguish qualitative classes or elements of a system.

In data science and machine learning, relabeling refers to updating or correcting the target labels in a dataset. This may occur when labels are found inaccurate, when a labeling scheme is revised, or when a mapping from old labels to new labels is required. Relabeling can affect model training, evaluation, and reproducibility, so researchers document the mapping and version the dataset. When class labels are arbitrary or misaligned with a reference, relabeling may be used to maximize agreement with ground truth.

In image processing and computer vision, relabeling is often applied to connected components labeling, where initially assigned labels are rewritten to occupy a standardized or consecutive range. Relabeling can also help maintain consistent label IDs across frames in video processing or across processing stages.

In graph theory and related areas, relabeling typically means applying a permutation to vertex labels. This is useful for canonical labeling, isomorphism testing, or storage optimization, where the numerical labels convey no intrinsic meaning beyond identity.

Practical considerations include maintaining traceability, documenting changes, and assessing the impact on analyses. Good practice involves version control of label mappings, clear justification for changes, and ensuring reproducibility of results.