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

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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.

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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).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Zhiguo Ding, Minrui Fei & Dajun Du

  2. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China

    Zhiguo Ding & Fan Yang

Authors
  1. Zhiguo Ding
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  2. Minrui Fei
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  3. Dajun Du
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  4. Fan Yang
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Correspondence to Zhiguo Ding.

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

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

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Communicated by Y. Jin.

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Ding, Z., Fei, M., Du, D. et al. Streaming data anomaly detection method based on hyper-grid structure and online ensemble learning. Soft Comput 21, 5905–5917 (2017). https://doi.org/10.1007/s00500-016-2258-z

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