Skip to main content
SpringerLink
Log in
Book cover

International Conference in Communications, Signal Processing, and Systems

CSPS 2018: Communications, Signal Processing, and Systems pp 225–232Cite as

Research on Concept-Drifting Data Stream Based on Fuzzy Integral Ensemble Classifier System

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 517))

Abstract

With the arrival of the era of big data, a large amount of data stream generates in the real world. However, the existence of concept drift has brought great challenges to data stream classification. Therefore, this paper proposed an ensemble classifier system based on fuzzy integral to solve the above problem. And after the experimental evaluation, we can approve the proposed algorithm outperforms other algorithms in terms of classification performance and the ability to adapt to new concepts efficiently.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Brazilian Symposium on Artificial Intelligence, pp. 286–295. Springer Berlin Heidelberg (2004)

    Google Scholar 

  2. Peter, V., Abraham, B.: Entropy-based concept shift detection. In: International Conference on Data Mining, pp. 1113–1118. IEEE (2006)

    Google Scholar 

  3. Nishida, K., Shimada, S., Ishikawa, S., Yamauchi, K.: Detecting sudden concept drift with knowledge of human behavior. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 3261–3267. IEEE (2008)

    Google Scholar 

  4. Wang, H., Yu P.S., Jiawei, H.: Mining concept-drifting data streams using ensemble classifiers. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM (2003)

    Google Scholar 

  5. Street, W.N., Kim, Y.S.: A streaming ensemble algorithm (SEA) for large-scale classification. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377–382. ACM (2001)

    Google Scholar 

  6. Chu, F., Wang, Y., Carlo, Z.: An adaptive learning approach for noisy data streams. In: IEEE International Conference on Data Mining, pp. 351–354. IEEE Computer Society (2004)

    Google Scholar 

Download references

Acknowledgements.

This paper is supported by the Natural Science Foundation of China (61271411), Natural Youth Science Foundation of China (61501326). It is also supported by Tianjin Research Program of Application Foundation and Advanced Technology (15JCZDJC31500) and Tianjin Science Foundation (16JCYBJC16500).

Author information

Authors and Affiliations

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

    Baoju Zhang, Yidi Chen & Lei Xue

Authors
  1. Baoju Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yidi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baoju Zhang .

Editor information

Editors and Affiliations

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

    Qilian Liang

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

    Xin Liu

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

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, B., Chen, Y., Xue, L. (2020). Research on Concept-Drifting Data Stream Based on Fuzzy Integral Ensemble Classifier System. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6508-9_29

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6507-2

  • Online ISBN: 978-981-13-6508-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation