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N°Spécial De Revue/Special Issue Proceedings of Machine Learning Research Année : 2021

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

Résumé

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.
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Dates et versions

hal-03345735 , version 1 (20-09-2021)

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Myrto Limnios, Nathan Noiry, Stéphan Clémençon. Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics. Proceedings of Machine Learning Research, 154, 2021, Proceedings of Machine Learning Research. ⟨hal-03345735⟩
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