  The ability to collect and store ever more massive databases has been
accompanied by the need to process them efficiently. In many cases, most
observations have the same behavior, while a probable small proportion of these
observations are abnormal. Detecting the latter, defined as outliers, is one of
the major challenges for machine learning applications (e.g. in fraud detection
or in predictive maintenance). In this paper, we propose a methodology
addressing the problem of outlier detection, by learning a data-driven scoring
function defined on the feature space which reflects the degree of abnormality
of the observations. This scoring function is learnt through a well-designed
binary classification problem whose empirical criterion takes the form of a
two-sample linear rank statistics on which theoretical results are available.
We illustrate our methodology with preliminary encouraging numerical
experiments.
