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A review on ranking problems in statistical learning
Ranking problems, also known as preference learning problems, define a widely
spread class of statistical learning problems with many applications, including
fraud detection, document ranking, medicine, credit risk screening, image
ranking or media memorability. In this article, we systematically review
different types of instance ranking problems, i.e., ranking problems that
require the prediction of an order of the response variables, and the
corresponding loss functions resp. goodness criteria. We discuss the
difficulties when trying to optimize those criteria. As for a detailed and
comprehensive overview of existing machine learning techniques to solve such
ranking problems, we systemize existing techniques and recapitulate the
corresponding optimization problems in a unified notation. We also discuss to
which of the ranking problems the respective algorithms are tailored and
identify their strengths and limitations. Computational aspects and open
research problems are also considered