Data perturbation for outlier detection ensembles

Zimek, Arthur, Campello, Ricardo J.G.B., and Sander, Jörg (2014) Data perturbation for outlier detection ensembles. In: Proceedings of the 26th International Conference on Scientific and Statistical Database Management, pp. 1-12. From: SSDBM 2014: 26th International Conference on Scientific and Statistical Database Management, 30 June - 2 July 2014, Aalborg, Denmark.

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Abstract

Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning of diverse models and combining these diverse models in an appropriate way. We propose data perturbation as a new technique to induce diversity in individual outlier detectors as well as a rank accumulation method for the combination of the individual outlier rankings in order to construct an outlier detection ensemble. In an extensive evaluation, we study the impact, potential, and shortcomings of this new approach for outlier detection ensembles. We show that this ensemble can significantly improve over weak performing base methods.

Item ID: 46781
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4503-2722-0
Date Deposited: 16 May 2017 02:56
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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