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Soft Computing

, Volume 21, Issue 20, pp 5905–5917 | Cite as

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

  • Zhiguo DingEmail author
  • Minrui Fei
  • Dajun Du
  • Fan Yang
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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.

Keywords

Hyper-grid structure Online ensemble learning Anomaly detection Streaming data 
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Notes

Acknowledgments

This study was funded by the National High Technology Research and Development Program of China (Grant No. 2011AA040103-7), the National Key Scientific Instrument and Equipment Development Project (Grant No. 2012YQ15008703), The Open Project of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial (Grant No. ZC323014100), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY13F020015), National Science Foundation of China (Grant No. 61104089), Science and Technology Commission of Shanghai Municipality (Grant No. 11JC1404000), Shanghai Rising-Star Program (Grant No. 13QA1401600).

Compliance with ethical standards

Conflict of interest

The four authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina
  2. 2.College of Mathematics, Physics and Information EngineeringZhejiang Normal UniversityJinhuaChina

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