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Regression-Based Outlier Detection of Sensor Measurements Using Independent Variable Synthesis

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Data Science (ICDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9208))

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Abstract

We present an improved outlier detection method using a regression model. A synthesized signal using the measurements of different sensors is applied for the estimation of the model parameters. The artificial and real dataset are used to verify the proposed method. The preliminary experiments show improvement in the regression-based outlier detection method.

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References

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Acknowledgments

This research was supported by a grant (14SCIP-B065985-02) from the Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean government and the Korea Agency for Infrastructure Technology Advancement(KAIA).

Author information

Authors and Affiliations

  1. Department of Technology & Systems Management, Induk University, Nowon-gu, Seoul, 139-749, South Korea

    Chang Mok Park

  2. Department of Construction Information Engineering, Induk University, Nowon-gu, Seoul, 139-749, South Korea

    Jesung Jeon

Authors
  1. Chang Mok Park
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  2. Jesung Jeon
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Corresponding author

Correspondence to Chang Mok Park .

Editor information

Editors and Affiliations

  1. University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Xi'an Jiaotong University, Xi'an Jiaotong, China

    Wei Huang

  3. Chinese Academy of Sciences, Beijing, China

    Yong Shi

  4. University of Illinois Chicago, CHICAGO, Illinois, USA

    Philip S. Yu

  5. Fudan University, Shanghai, China

    Yangyong Zhu

  6. Research Center on Fictitious Econo, Chinese Academy of Sciences, Beijing, China

    Yingjie Tian

  7. University of Technology, Sydney, Australia

    Peng Zhang

  8. Victoria University, Melbourne, Victoria, Australia

    Jing He

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Park, C.M., Jeon, J. (2015). Regression-Based Outlier Detection of Sensor Measurements Using Independent Variable Synthesis. In: Zhang, C., et al. Data Science. ICDS 2015. Lecture Notes in Computer Science(), vol 9208. Springer, Cham. https://doi.org/10.1007/978-3-319-24474-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-24474-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24473-0

  • Online ISBN: 978-3-319-24474-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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