A simple probabilistic approach to learning from positive and unlabeled examples

Abstract

We propose a simple probabilistic approach to learning from positive and unlabeled examples, and show experimentally that it can approximate or outperform other state-ofthe- Art approaches to this problem in spite of its simplicity. By employing a linear-time learning algorithm such as PrTFIDF, our approach can be highly efficient and scalable.