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

Abstract : 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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Contributor : Myrto Limnios Connect in order to contact the contributor
Submitted on : Monday, September 20, 2021 - 12:53:17 PM
Last modification on : Thursday, April 7, 2022 - 1:58:35 PM
Long-term archiving on: : Tuesday, December 21, 2021 - 6:04:07 PM


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  • HAL Id : hal-03345735, version 1
  • ARXIV : 2109.09590


Myrto Limnios, Nathan Noiry, Stéphan Clémençon. Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics. LIDTA21: 3rd International Workshop on Learning with Imbalanced Domains: Theory and Applications, Proceedings of Machine Learning Research, 154, 2021, Proceedings of Machine Learning Research. ⟨hal-03345735⟩



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