A review on ranking problems in statistical learning

Ranking problems define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking or medicine. In this article, we systematically describe different types of ranking problems and investigate existing empirical risk minimization techniques to solve such ranking problems. Furthermore, we discuss whether a Boosting-type algorithm for continuous ranking problems is achievable by using surrogate loss functions.