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

Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics