Imprecise Label

Imprecise label learning addresses the challenge of training machine learning models with incomplete or uncertain labels, a common problem in many real-world datasets where obtaining perfectly precise labels is costly or impossible. Current research focuses on developing unified frameworks that can handle various forms of imprecise labels, such as intervals or multiple label candidates, often employing expectation-maximization algorithms or hybrid models to integrate both precise and imprecisely labeled data. This field is significant because it allows for the effective utilization of large datasets with imperfect annotations, improving the robustness and applicability of machine learning models across diverse domains, particularly in healthcare where precise labeling is often difficult and expensive.