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Wisdom of the contexts : active ensemble learning for contextual anomaly detection
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- Calikus, Ece, 1990- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Nowaczyk, Sławomir, 1978- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Bouguelia, Mohamed-Rafik, 1987- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- (creator_code:org_t)
- 2022-10-04
- 2022
- English.
- In: Data mining and knowledge discovery. - New York : Springer-Verlag New York. - 1384-5810 .- 1573-756X. ; 36, s. 2410-2458
- Journal article (peer-reviewed)
Abstract
Subject headings
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- In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we propose a novel approach, called WisCon (Wisdom of the Contexts), to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. We estimate the importance of each context using an active learning approach with a novel query strategy. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual, and non-contextual anomaly detectors) on 18 datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary. © 2022, The Author(s).
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Anomaly detection
- Active learning
- Contextual anomaly detection
- Ensemble learning
- Active learning
- Smart Cities and Communities
- Smarta städer och samhällen
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- ref (subject category)
- art (subject category)
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