Heuristic Label

Heuristic labeling involves using rules or approximations to automatically assign labels to data, reducing the need for extensive manual annotation. Current research focuses on improving the accuracy and efficiency of heuristic labeling, particularly within the context of weak supervision for machine learning, exploring methods like probabilistic generative models and incorporating label-derived features to guide model training in tasks such as 3D segmentation and multi-label classification. This approach is crucial for addressing data scarcity in various domains, including medicine and legal text processing, enabling the development and application of machine learning models where large, manually labeled datasets are impractical or infeasible.