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 seq...

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Bibliographic Details
Published in:The Artificial intelligence review Vol. 53; no. 5; pp. 3787 - 3812
Main Authors: Domingues, Rémi, Michiardi, Pietro, Barlet, Jérémie, Filippone, Maurizio
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.06.2020
Springer
Springer Nature B.V
Subjects:
Algorithms
Analysis
Anomalies
Artificial Intelligence
Candidates
Computer Science
Datasets
Embedded systems
Experiments
Fraud
Fraud prevention
Genomics
Identification methods
Intrusion
Probability
Reaction time
Response time
Selection criteria
Sequences
Temporal data
Outlier detection
Discrete sequences
Fraud detection
Novelty detection
Anomaly detection
ISSN:0269-2821, 1573-7462
Online Access:Get full text
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Summary: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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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-019-09779-4