A comparative evaluation of novelty detection algorithms for discrete sequences

• Published in: arXiv.org
• Main Authors: Domingues, Rémi, Michiardi, Pietro, Barlet, Jérémie, Filippone, Maurizio
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 29.11.2019
• Subjects: Algorithms; Anomalies; Computer Science - Learning; Cybersecurity; Datasets; Fraud; Identification methods; Intrusion detection systems; Response time; Statistics - Machine Learning

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 the novelty detection problem applied to discrete sequences. The objective...