Positive And Unlabeled Learning Algorithms And Applications: A Survey

This paper will address the Positive and Unlabeled learning problem (PU learning) and its importance in the growing field of semi-supervised learning. In most real-world classification applications, well labeled data is expensive or impossible to obtain. We can often label a small subset of data as belonging to the class of interest. It is frequently impractical to manually label all data we are not interested in. We are left with a small set of positive labeled items of interest and a large set of unknown and unlabeled data. Learning a model for this is the PU learning problem. In this paper, we explore several applications for PU learning including examples in biological/medical, business, security, and signal processing. We then survey the literature for new and existing solutions to the PU learning problem.