Positive Unlabeled Learning

Positive-Unlabeled Learning (PU learning) addresses the challenge of training binary classifiers with only positive examples and unlabeled data, a common scenario in many real-world applications where obtaining negative labels is expensive or impossible. Current research focuses on improving the accuracy and robustness of PU learning algorithms, exploring various model architectures including random forests, neural networks, and graph convolutional networks, often incorporating techniques like pseudo-labeling, density estimation, and self-supervision to handle the inherent data imbalance and uncertainty. The ability to effectively leverage unlabeled data has significant implications for diverse fields, including anomaly detection, recommendation systems, and medical diagnosis, where labeled data is often scarce and costly to acquire.