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A comparative evaluation of novelty detection algorithms for discrete sequences


The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of candidate methods for the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods’ performance, key selection criteria to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.

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The authors wish to thank the Amadeus Middleware Fraud Detection team directed by Virginie Amar and Jérémie Barlet, led by the product owner Christophe Allexandre and composed of Jean-Blas Imbert, Jiang Wu, Yang Pu and Damien Fontanes for building the rights, transactions-fr and transactions-mo datasets. MF gratefully acknowledges support from the AXA Research Fund.

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Correspondence to Rémi Domingues.

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Domingues, R., Michiardi, P., Barlet, J. et al. A comparative evaluation of novelty detection algorithms for discrete sequences. Artif Intell Rev (2019). https://doi.org/10.1007/s10462-019-09779-4

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  • Novelty detection
  • Discrete sequences
  • Temporal data
  • Fraud detection
  • Outlier detection
  • Anomaly detection