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Concept Drift Detection Based on Kolmogorov–Smirnov Test

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Artificial Intelligence in China

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 572))

Abstract

With the advancement of information society, a large amount of data, which is in the form of stream, has been produced in many fields. As a result of its extensive application in the fields of sensor networks, banking and telecommunications, data stream mining is obtaining more attention. One of the most challenging steps to learn from data stream is to react to concept drift, as most of the existing data stream algorithms only deal with abrupt or gradual concept drifts. The existing work of detecting concept drift is mostly based on the changing of error rate of single window, making it difficult to be universally applied to different types of concept drifts. A method of detecting concept drift is proposed in this paper based on Kolmogorov–Smirnov test (K–S test).

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References

  1. Stiglic G, Kokol P (2011) Interpretability of sudden concept drift in medical informatics domain. In: 2011 IEEE 11th international conference on data mining workshops, pp 609–613

    Google Scholar 

  2. Sun J, Li H, Adeli H (2013) Concept drift-oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction. IEEE Trans Syst Man Cybern: Syst 43(4):801–813

    Article  Google Scholar 

  3. Gama J, Medas P, Castillo G (2004) Learning with drift detection. In: Brazilian symposium on artificial intelligence, pp 286–295

    Chapter  Google Scholar 

  4. Baena-Garcıa M, del Campo-Ávila J, Fidalgo R (2006) Early drift detection method. In: Fourth international workshop on knowledge discovery from data streams, pp 77–86

    Google Scholar 

  5. Vorburger P, Bernstein A (2006) Entropy-based concept shift detection. pp. 113–118

    Google Scholar 

  6. Ross GJ, Adams NM, Tasoulis DK et al (2012) Exponentially weighted moving average charts for detecting concept drift. Pattern Recogn Lett 33(16): 191–198

    Article  Google Scholar 

  7. Frias-Blanco I, Campo-Avila JD, Ramos-Jimenez G et al (2015) Online and non-parametric drift detection methods based on hoeffding’s bounds. IEEE Trans Knowl Data Eng 27(3):810–823

    Article  Google Scholar 

  8. Farid DM, Li Z, Hossain A et al (2013) An adaptive ensemble classifier for mining concept drifting data streams. Expert Sys Appl 40(15):5895–5906

    Article  Google Scholar 

  9. Ada I, Berthold MR (2014) EVE: a framework for event detection. Evolving Sys 4(1):1–10

    Article  Google Scholar 

  10. Sakthithasan S, Pears R, Yun SK (2013) One pass concept change detection for data streams. Adv Knowl Discov Data Min

    Google Scholar 

  11. Pears R, Sakthithasan S, Yun SK (2014) Detecting concept change in dynamic data stream. Mach Learn 97(3):259–293

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This paper is supported by Natural Youth Science Foundation of China (61501326), the National Natural Science Foundation of China (61731006).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, 300387, China

    Zhixiong Wang & Wei Wang

Authors
  1. Zhixiong Wang
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  2. Wei Wang
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Corresponding author

Correspondence to Wei Wang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin, China

    Wei Wang

  3. Department of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  4. School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Xin Liu

  5. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China

    Zhenyu Na

  6. School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China

    Bingcai Chen

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Wang, Z., Wang, W. (2020). Concept Drift Detection Based on Kolmogorov–Smirnov Test. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z., Chen, B. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-15-0187-6_31

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  • DOI: https://doi.org/10.1007/978-981-15-0187-6_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0186-9

  • Online ISBN: 978-981-15-0187-6

  • eBook Packages: EngineeringEngineering (R0)

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