- Title
- Revisiting attribute independence assumption in probabilistic unsupervised anomaly detection
- Creator
- Aryal, Sunil; Ting, Kaiming; Haffari, Gholamreza
- Date
- 2016
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161665
- Identifier
- vital:12513
- Identifier
-
https://doi.org/10.1007/978-3-319-31863-9_6
- Identifier
- ISBN:9783319318622
- Abstract
- In this paper, we revisit the simple probabilistic approach of unsupervised anomaly detection by estimating multivariate probability as a product of univariate probabilities, assuming attributes are generated independently. We show that this simple traditional approach performs competitively to or better than five state-of-the-art unsupervised anomaly detection methods across a wide range of data sets from categorical, numeric or mixed domains. It is arguably the fastest anomaly detector. It is one order of magnitude faster than the fastest state-of-the- art method in high dimensional data sets.
- Publisher
- Springer
- Relation
- 11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016 - Auckland, New Zealand, 19th April, 2016 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9650 p. 73-86
- Rights
- This metadata is freely available under a CCO license
- Subject
- Fast anomaly detection; Independence assumption; Big data
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