Skip to main content
SpringerLink
Log in

An Augmented Learning Approach for Multiple Data Streams Under Concept Drift

  • Conference paper
  • First Online:
AI 2023: Advances in Artificial Intelligence (AI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14471))

Included in the following conference series:

  • 551 Accesses

Abstract

Multiple data streams learning attracts more and more attention recently. Different from learning a single data stream, the uncertain and complex occurrence of concept drift in multiple data streams, bring challenges in real-time learning task. To address this issue, this paper proposed a method called time-warping-based concept drift learning method (TW-CDM) for dealing with multiple data streams. First, a time-warping-based drift identification process is given to recognize the drift region. Second, an augmented learning process is developed by crossly using the located region data. Finally, a selectively augmented learning process is given to reduce the influence of different drift severity. The proposed method is evaluated on both synthetic and real-world datasets, and compared with benchmark methods. The experiment results show the efficiency of the proposed method.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    https://github.com/kunkun111/TW-CDM.

References

  1. Álvarez, V., Mazuelas, S., Lozano, J.A.: Minimax classification under concept drift with multidimensional adaptation and performance guarantees. In: International Conference on Machine Learning, pp. 486–499. PMLR (2022)

    Google Scholar 

  2. Baena-Garcıa, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A.: Early drift detection method. In: In 4th International Workshop on Knowledge Discovery from Data Streams. Citeseer (2006)

    Google Scholar 

  3. Balzanella, A., Verde, R.: Histogram-based clustering of multiple data streams. Knowl. Inf. Syst. 62(1), 203–238 (2020)

    Article  MATH  Google Scholar 

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

    Google Scholar 

  5. Chandra, S., Haque, A., Khan, L., Aggarwal, C.: An adaptive framework for multistream classification. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 1181–1190. Indianapolis, IN, USA (2016)

    Google Scholar 

  6. Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)

    Article  Google Scholar 

  7. Fang, Z., Lu, J., Liu, F., Xuan, J., Zhang, G.: Open set domain adaptation: theoretical bound and algorithm. IEEE Trans. Neural Netw. Learn. Syst. 32(10), 4309–4322 (2020)

    Article  MathSciNet  Google Scholar 

  8. Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. LNCS, vol. 3171, pp. 286–295. Springer, Berlin, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28645-5_29

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

    Article  MATH  Google Scholar 

  10. Haque, A., Wang, Z., Chandra, S., Dong, B., Khan, L., Hamlen, K.W.: Fusion: an online method for multistream classification. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 919–928. Singapore (2017)

    Google Scholar 

  11. Jaysawal, B.P., Huang, J.W.: PSP-AMS: progressive mining of sequential patterns across multiple streams. ACM Trans. Knowl. Discov. Data 13(1), 1–23 (2018)

    Article  Google Scholar 

  12. Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: an ensemble method for drifting concepts. J. Mach. Learn. Res. 8, 2755–2790 (2007)

    MATH  Google Scholar 

  13. Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Woźniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132–156 (2017)

    Article  Google Scholar 

  14. Li, W., Yang, X., Liu, W., Xia, Y., Bian, J.: DDG-DA: data distribution generation for predictable concept drift adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4092–4100 (2022)

    Google Scholar 

  15. Liu, A., Lu, J., Liu, F., Zhang, G.: Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recogn. 76, 256–272 (2018)

    Article  Google Scholar 

  16. Liu, A., Song, Y., Zhang, G., Lu, J.: Regional concept drift detection and density synchronized drift adaptation. In: IJCAI International Joint Conference on Artificial Intelligence (2017)

    Google Scholar 

  17. Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346–2363 (2018)

    Google Scholar 

  18. Lu, J., Niu, L., Zhang, G.: A situation retrieval model for cognitive decision support in digital business ecosystems. IEEE Trans. Ind. Electron. 60(3), 1059–1069 (2012)

    Article  Google Scholar 

  19. Lu, J., Yang, X., Zhang, G.: Support vector machine-based multi-source multi-attribute information integration for situation assessment. Expert Syst. Appl. 34(2), 1333–1340 (2008)

    Article  Google Scholar 

  20. Lu, J., Zuo, H., Zhang, G.: Fuzzy multiple-source transfer learning. IEEE Trans. Fuzzy Syst. 28(12), 3418–3431 (2019)

    Article  Google Scholar 

  21. Mihai, N., Alexandru, M., Bala-Constantin, Z.: Multimodal emotion detection from multiple data streams for improved decision making. Procedia Comput. Sci. 214, 1082–1089 (2022)

    Article  Google Scholar 

  22. Nadeem, A., Verwer, S.: SECLEDS: sequence clustering in evolving data streams via multiple medoids and medoid voting. In: Amini, M.R., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. LNCS, vol. 13713, pp. 157–173. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-26387-3_10

  23. Otero, A., Félix, P., Márquez, D.G., García, C.A., Caffarena, G.: A fault-tolerant clustering algorithm for processing data from multiple streams. Inf. Sci. 584, 649–664 (2022)

    Article  Google Scholar 

  24. Sangma, J.W., Pal, V., Kumar, N., Kushwaha, R., et al.: FHC-NDS: fuzzy hierarchical clustering of multiple nominal data streams. IEEE Trans. Fuzzy Syst. 31(3), 786–798 (2022)

    Article  Google Scholar 

  25. Song, Y., Zhang, G., Lu, H., Lu, J.: A fuzzy drift correlation matrix for multiple data stream regression. In: 2020 IEEE International Conference on Fuzzy Systems, pp. 1–6. IEEE (2020)

    Google Scholar 

  26. Yu, E., Song, Y., Zhang, G., Lu, J.: Learn-to-adapt: concept drift adaptation for hybrid multiple streams. Neurocomputing 496, 121–130 (2022)

    Article  Google Scholar 

  27. Yu, H., Liu, W., Lu, J., Wen, Y., Luo, X., Zhang, G.: Detecting group concept drift from multiple data streams. Pattern Recogn. 134, 109113 (2023)

    Article  Google Scholar 

  28. Zhou, M., Song, Y., Zhang, G., Zhang, B., Lu, J.: An efficient Bayesian neural network for multiple data streams. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2021)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Australian Research Council under Grants DP200100700 and FL190100149.

Author information

Authors and Affiliations

  1. Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia

    Kun Wang, Jie Lu, Anjin Liu & Guangquan Zhang

Authors
  1. Kun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anjin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Lu .

Editor information

Editors and Affiliations

  1. The University of Sydney, Darlington, NSW, Australia

    Tongliang Liu

  2. Monash University, Clayton, VIC, Australia

    Geoff Webb

  3. The University of Newcastle, Callaghan, NSW, Australia

    Lin Yue

  4. CSIRO Data61, Sydney, NSW, Australia

    Dadong Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, K., Lu, J., Liu, A., Zhang, G. (2024). An Augmented Learning Approach for Multiple Data Streams Under Concept Drift. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14471. Springer, Singapore. https://doi.org/10.1007/978-981-99-8388-9_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8388-9_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8387-2

  • Online ISBN: 978-981-99-8388-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation