Ryan Ko

Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization

The need to learn from positive and unlabeled data, or PU learning, arises in many applications and has attracted increasing interest. While random forests are known to perform well on many tasks with positive and negative data, recent PU algorithms are generally based on deep neural networks, and t…