Anomaly Explanation : A Review

Autor: Olivier Pivert, Grégory Smits, Véronne Yepmo Tchaghe
Přispěvatelé: GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), A Symbolic and Human-centric view of dAta MANagement (SHAMAN), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), This research takes part of the SEA DEFENDER project funded by the French DGA (Directorate General of Armaments)., Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Data and Knowledge Engineering
Data and Knowledge Engineering, Elsevier, In press
Data and Knowledge Engineering, 2022, 137, pp.101946
ISSN: 0169-023X
Popis: International audience; Anomaly detection has been studied intensively by the data mining community for several years. As a result, many methods to detect anomalies have emerged, and others are still under development. But during the recent years, anomaly detection, just like a lot of machine learning tasks, is facing a wall. This wall, erected by the lack of trust of the final users, has slowed down the usage of these algorithms in the real-world situations for which they are designed. Having the best empirical accuracy is not enough anymore; there is a need for algorithms to explain their outputs to the users in order to increase their trust. Consequently, a new expression has emerged recently: eXplainable Artificial Intelligence (XAI). This expression, which gathers all the methods that provide explanations to the output of algorithms has gained popularity, especially with the outbreak of deep learning. A lot of work has been devoted to anomaly detection in the literature, but not as much to anomaly explanation. There is so much work on anomaly detection that several reviews can be found on the topic. In contrast, we were not able to find a survey on anomaly explanation in particular, while there are a lot of surveys on XAI in general or on XAI for neural networks for example. With this paper, we want to provide a comprehensive review of the anomaly explanation field. After a brief recall of some important anomaly detection algorithms, the anomaly explanation methods that we discovered in the literature will be classified according to a taxonomy that we define. This taxonomy stems from an analysis of what is really important when trying to explain anomalies.
Databáze: OpenAIRE
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