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Soft Computing
Erschienen in: Soft Computing 20/2017

08.07.2016 | Focus

Streaming data anomaly detection method based on hyper-grid structure and online ensemble learning

verfasst von: Zhiguo Ding, Minrui Fei, Dajun Du, Fan Yang

Erschienen in: Soft Computing | Ausgabe 20/2017

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Abstract

This paper proposes a novel online streaming data anomaly detection method. By using the new method, the improved \(L_{1}\) detection neighbor region optimizes the initial hyper-grid-based anomaly detection method by decreasing the quantity of neighbor detection region, and online ensemble learning adapts to the distribution evolving characteristic of streaming data and overcomes the difficulty of obtaining the optimal hyper-grid structure. To validate the proposed method, the paper uses a real-world dataset and two simulated datasets and finds out that the experimental results are near to the optimal results.
Vorheriger Artikel An improved immune system-inspired routing recovery scheme for energy harvesting wireless sensor networks
Nächster Artikel An online learning neural network ensembles with random weights for regression of sequential data stream

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Metadaten
Titel
Streaming data anomaly detection method based on hyper-grid structure and online ensemble learning
verfasst von
Zhiguo Ding
Minrui Fei
Dajun Du
Fan Yang
Publikationsdatum
08.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 20/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2258-z

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