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Détection et localisation d'anomalies dans des données hétérogènes en utilisant des modèles graphiques non orientés mixtes

Abstract : This thesis revolves around an industrial need of Thales Système Aéroportés and the RBE2 combat radar equipping Dassault Rafale fighter aircraft. It develops a methodology for locating anomalies in heterogeneous data stream using a mixed, non-orientation and peer-to-peer graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned from a data set that is assumed not to contain abnormal data. Anomaly localization algorithms use an adapted version of the CUSUM algorithm, whose decision function is based on the calculation of conditional likelihood ratios. This function allows the detection of variable anomalies per variable and the precise localization of the variables involved in the anomaly.
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https://pastel.archives-ouvertes.fr/tel-03219690
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Submitted on : Thursday, May 6, 2021 - 3:50:10 PM
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  • HAL Id : tel-03219690, version 1

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Romain Laby. Détection et localisation d'anomalies dans des données hétérogènes en utilisant des modèles graphiques non orientés mixtes. Machine Learning [stat.ML]. Télécom ParisTech, 2017. Français. ⟨NNT : 2017ENST0026⟩. ⟨tel-03219690⟩

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