Skip to main content
SpringerLink
Log in

Drift Detection over Non-stationary Data Streams Using Evolving Spiking Neural Networks

  • Conference paper
  • First Online:
Intelligent Distributed Computing XII (IDC 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 798))

Included in the following conference series:

Abstract

Drift detection in changing environments is a key factor for those active adaptive methods which require trigger mechanisms for drift adaptation. Most approaches are relied on a base learner that provides accuracies or error rates to be analyzed by an algorithm. In this work we propose the use of evolving spiking neural networks as a new form of drift detection, which resorts to the own architectural changes of this particular class of models to estimate the drift location without requiring any external base learner. By virtue of its inherent simplicity and lower computational cost, this embedded approach can be suitable for its adoption in online learning scenarios with severe resource constraints. Experiments with synthetic datasets show that the proposed technique is very competitive when compared to other drift detection techniques.

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

Access this chapter

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

Institutional subscriptions

References

  1. Zhou, Z.H., Chawla, N.V., Jin, Y., Williams, G.J.: Big data opportunities and challenges: discussions from data analytics perspectives. IEEE Comput. Intell. Mag. 9(4), 62–74 (2014)

    Article  Google Scholar 

  2. Alippi, C.: Intelligence for Embedded Systems. Springer, Heidelberg (2014)

    Book  Google Scholar 

  3. Domingos, P., Hulten, G.: A general framework for mining massive data streams. J. Comput. Graph. Stat. 12(4), 945–949 (2003)

    Article  MathSciNet  Google Scholar 

  4. Ditzler, G., Roveri, M., Alippi, C., Polikar, R.: Learning in nonstationary environments: a survey. IEEE Comput. Intell. Mag. 10(4), 12–25 (2015)

    Article  Google Scholar 

  5. Khamassi, I., Sayed-Mouchaweh, M., Hammami, M., Ghédira, K.: Discussion and review on evolving data streams and concept drift adapting. Evolving Syst. 9(1), 1–23 (2018)

    Article  Google Scholar 

  6. Gonçalves Jr., P.M., de Carvalho Santos, S.G., Barros, R.S., Vieira, D.C.: A comparative study on concept drift detectors. Expert Syst. Appl. 41(18), 8144–8156 (2014)

    Article  Google Scholar 

  7. Demšar, J., Bosnić, Z.: Detecting concept drift in data streams using model explanation. Expert Syst. Appl. 92, 546–559 (2018)

    Article  Google Scholar 

  8. Minku, L.L., Yao, X.: DDD: a new ensemble approach for dealing with concept drift. IEEE Trans. Knowl. Data Eng. 24(4), 619–633 (2012)

    Article  Google Scholar 

  9. Gonçalves Jr., P.M., De Barros, R.S.M.: RCD: a recurring concept drift framework. Pattern Recogn. Lett. 34(9), 1018–1025 (2013)

    Article  Google Scholar 

  10. Dehghan, M., Beigy, H., ZareMoodi, P.: A novel concept drift detection method in data streams using ensemble classifiers. Intell. Data Anal. 20(6), 1329–1350 (2016)

    Article  Google Scholar 

  11. Brzezinski, D., Stefanowski, J.: Ensemble diversity in evolving data streams. In: International Conference on Discovery Science, pp. 229–244. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

  12. Lobo, J.L., Del Ser, J., Bilbao, M.N., Perfecto, C., Salcedo-Sanz, S.: DRED: an evolutionary diversity generation method for concept drift adaptation in online learning environments. Appl. Soft Comput. 68, 693–709 (2017)

    Article  Google Scholar 

  13. Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)

    Book  Google Scholar 

  14. Soltic, S., Kasabov, N.: Knowledge extraction from evolving spiking neural networks with rank order population coding. Int. J. Neural Syst. 20(06), 437–445 (2010)

    Article  Google Scholar 

  15. Schliebs, S., Kasabov, N.: Evolving spiking neural network: a survey. Evolving Syst. 4(2), 87–98 (2013)

    Article  Google Scholar 

  16. Gama, J., Zliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)

    Article  Google Scholar 

  17. Wald, A.: Sequential Analysis. Courier Corporation, New York City (1973)

    MATH  Google Scholar 

  18. Page, E.S.: Continuous inspection schemes. Biometrika 41(1/2), 100–115 (1954)

    Article  MathSciNet  Google Scholar 

  19. Ross, G.J., Adams, N.M., Tasoulis, D.K., Hand, D.J.: Exponentially weighted moving average charts for detecting concept drift. Pattern Recogn. Lett. 33(2), 191–198 (2012)

    Article  Google Scholar 

  20. Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, SIAM, pp. 443–448 (2007)

    Chapter  Google Scholar 

  21. Minku, L.L.: Online ensemble learning in the presence of concept drift. Ph.D. thesis, University of Birmingham (2011)

    Google Scholar 

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

    Chapter  Google Scholar 

  23. Baena-García, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavaldà, R., Morales-Bueno, R.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams (2006)

    Google Scholar 

  24. Bach, S.H., Maloof, M.A.: Paired learners for concept drift. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 23–32. IEEE (2008)

    Google Scholar 

  25. Sobhani, P., Beigy, H.: New drift detection method for data streams. In: Adaptive and intelligent systems, pp. 88–97. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  26. Nishida, K., Yamauchi, K.: Detecting concept drift using statistical testing. In: International Conference on Discovery Science, pp. 264–269. Springer, Heidelberg (2007)

    Google Scholar 

  27. Barros, R.S., Cabral, D.R., Gonçalves Jr., P.M., Santos, S.G.: RDDM: reactive drift detection method. Expert Syst. Appl. 90, 344–355 (2017)

    Article  Google Scholar 

  28. Wang, J., Belatreche, A., Maguire, L., Mcginnity, T.M.: An online supervised learning method for spiking neural networks with adaptive structure. Neurocomputing 144, 526–536 (2014)

    Article  Google Scholar 

  29. Wang, J., Belatreche, A., Maguire, L., McGinnity, M.: Online versus offline learning for spiking neural networks: a review and new strategies. In: 2010 IEEE 9th International Conference on Cybernetic Intelligent Systems (CIS), pp. 1–6. IEEE (2010)

    Google Scholar 

  30. Wang, J., Belatreche, A., Maguire, L.P., McGinnity, T.M.: SpikeTemp: an enhanced rank-order-based learning approach for spiking neural networks with adaptive structure. IEEE Trans. Neural Netw. Learn. Syst. 28(1), 30–43 (2017)

    Article  Google Scholar 

  31. Kasabov, N.K.: Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  32. Thorpe, S.J., Gautrais, J.: Rapid visual processing using spike asynchrony. In: Advances in Neural Information Processing Systems, pp. 901–907 (1997)

    Google Scholar 

  33. Bohte, S.M., Kok, J.N., La Poutre, H.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1–4), 17–37 (2002)

    Article  Google Scholar 

  34. Thorpe, S., Gautrais, J.: Rank order coding. In: Computational Neuroscience, pp. 113–118. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  35. Minku, L.L., White, A.P., Yao, X.: The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans. Knowl. Data Eng. 22(5), 730–742 (2010)

    Article  Google Scholar 

  36. Frías-Blanco, I., del Campo-Ávila, J., Ramos-Jiménez, G., Morales-Bueno, R., Ortiz-Díaz, A., Caballero-Mota, Y.: Online and non-parametric drift detection methods based on hoeffdings bounds. IEEE Trans. Knowl. Data Eng. 27(3), 810–823 (2015)

    Article  Google Scholar 

  37. Gao, J., Ding, B., Fan, W., Han, J., Philip, S.Y.: Classifying data streams with skewed class distributions and concept drifts. IEEE Internet Comput. 12(6) (2008)

    Article  Google Scholar 

  38. Pears, R., Sakthithasan, S., Koh, Y.S.: Detecting concept change in dynamic data streams. Mach. Learn. 97(3), 259–293 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the EU project Pacific Atlantic Network for Technical Higher Education and Research - PANTHER (grant number 2013-5659/004-001 EMA2), and by the Basque Government through the EMAITEK program.

Author information

Authors and Affiliations

  1. TECNALIA, 48160, Derio, Spain

    Jesus L. Lobo, Javier Del Ser & Ibai Laña

  2. University of the Basque Country UPV/EHU, 48013, Bilbao, Spain

    Javier Del Ser & Miren Nekane Bilbao

  3. Basque Center for Applied Mathematics (BCAM), 48009, Bilbao, Spain

    Javier Del Ser

  4. KEDRI - Auckland University of Technology (AUT), 1010, Auckland, New Zealand

    Nikola Kasabov

Authors
  1. Jesus L. Lobo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javier Del Ser
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ibai Laña
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miren Nekane Bilbao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nikola Kasabov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jesus L. Lobo .

Editor information

Editors and Affiliations

  1. Basque Center for Applied Mathematics (BCAM), TECNALIA, University of the Basque Country (UPV/EHU), Derio, Bizkaia, Spain

    Javier Del Ser

  2. TECNALIA, Derio, Bizkaia, Spain

    Eneko Osaba

  3. University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain

    Miren Nekane Bilbao

  4. Computer Science Department, University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Javier J. Sanchez-Medina

  5. OpenIoT Reesarch Unit, FBK CREATE-NET, Trento, Italy

    Massimo Vecchio

  6. Department of Design Engineering and Mathematics, Middlesex University, London, UK

    Xin-She Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lobo, J.L., Del Ser, J., Laña, I., Bilbao, M.N., Kasabov, N. (2018). Drift Detection over Non-stationary Data Streams Using Evolving Spiking Neural Networks. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99626-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99625-7

  • Online ISBN: 978-3-319-99626-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation