Cost-Sensitive Unbiased Risk Estimation for Multi-Class Positive-Unlabeled Learning
NeutralArtificial Intelligence
A new study on positive-unlabeled (PU) learning has been released, focusing on the challenges of multi-class scenarios where only positive and unlabeled data are available. This research is significant because it addresses the common issue in real-world applications where obtaining reliable negative data is often difficult or expensive. The findings aim to improve unbiased risk estimation in PU learning, which is crucial for enhancing performance in various machine learning tasks.
— Curated by the World Pulse Now AI Editorial System

