Skip to main content
SpringerLink
Log in

SABeDM: a sliding adaptive beta distribution model for concept drift detection in a dynamic environment

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The challenge of concept drift detection is crucial in machine learning, especially in dynamic contexts where the underlying data distribution can vary over time. For the purpose of identifying concept drift, we suggest a sliding adaptive beta distribution model (SABeDM) in this study. SABeDM combines the adaptive sliding window and beta distribution techniques to track modifications in the underlying distribution of the data stream. Several synthetic and real-world datasets are used to assess the proposed model, and it is then contrasted with cutting-edge drift detection systems. Regarding detecting true positive, false positive, false negative, and delay, our experimental results demonstrate that SABeDM works better than the currently used methods (SRP, ADWIN, DDM, and EDDM). Accuracy, precision, recall, and F1-score were also utilised as evaluation criteria. When used in a variety of applications, such as online learning, data stream mining, and real-time monitoring systems, SABeDM offers an effective and fast way to identify concept drift in a dynamic context. The proposed approach is a promising tool for machine learning practitioners to use in practical applications since it can help to enhance the dependability and accuracy of decision-making systems in dynamic situations.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

References

  1. Guo H, Li H, Sun N, Ren Q, Zhang A, Wang W (2023) Concept drift detection and accelerated convergence of online learning. Knowl Inf Syst 65(3):1005–1043. https://doi.org/10.1007/s10115-022-01790-6

    Article  Google Scholar 

  2. Widmer G (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–101. https://doi.org/10.1007/bf00116900

    Article  MathSciNet  Google Scholar 

  3. Abbasi A, Javed AR, Chakraborty C, Nebhen J, Zehra W, Jalil Z (2021) ElStream: an ensemble learning approach for concept drift detection in dynamic social big data stream learning. IEEE Access 9:66408–66419. https://doi.org/10.1109/ACCESS.2021.3076264

    Article  Google Scholar 

  4. Shahraki A, Abbasi M, Taherkordi A, Jurcut AD (2022) A comparative study on online machine learning techniques for network traffic streams analysis. Comput Netw 207:108836. https://doi.org/10.1016/j.comnet.2022.108836

    Article  Google Scholar 

  5. Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2013) A survey on concept drift adaptation. ACM Comput Surv 1(1):35. https://doi.org/10.1145/0000000.0000000

    Article  Google Scholar 

  6. Bifet A, Holmes G, Pfahringer B, Kirkby R, and Gavaldà R (2009) New ensemble methods for evolving data streams. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 139–147. https://doi.org/10.1145/1557019.1557041

  7. Yan MMW (2020) Accurate detecting concept drift in evolving data streams. ICT Express 6(4):332–338. https://doi.org/10.1016/j.icte.2020.05.011

    Article  Google Scholar 

  8. Gama J, Žliobaitundefined I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv. https://doi.org/10.1145/2523813

    Article  Google Scholar 

  9. Wald A (2004) Sequential analysis. Courier Corporation

  10. Page ES (1954) Continuous inspection schemes. Biometrika 41(1/2):100. https://doi.org/10.2307/2333009

    Article  MathSciNet  Google Scholar 

  11. Gama J, Medas P, Castillo G, and Rodrigues P (2004) Learning with drift detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 3171, no Sept, pp 286–295. https://doi.org/10.1007/978-3-540-28645-5_29

  12. Baena-Garcia M, Del Campo-Avila J, Fidalgo R, Bifet A, Gavalda R, and Morales-bueno R (2006) Early Drift Detection Method. In: 4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams, vol 6, pp 77–86

  13. Ross GJ, Adams NM, Tasoulis DK, Hand DJ (2012) Exponentially weighted moving average charts for detecting concept drift. Pattern Recogn Lett 33(2):191–198. https://doi.org/10.1016/j.patrec.2011.08.019

    Article  ADS  Google Scholar 

  14. Barros RSM, Cabral DRL, Gonçalves PM, Santos SGTC (2017) RDDM: reactive drift detection method. Expert Syst Appl 90:344–355. https://doi.org/10.1016/j.eswa.2017.08.023

    Article  Google Scholar 

  15. Angelopoulos A et al (2021) Impact of classifiers to drift detection method: a comparison. In: Proceedings of the 22nd Engineering Applications of Neural Networks Conference, pp 399–410

  16. Liu Z, Loo CK, Seera M (2019) Meta-cognitive recurrent recursive Kernel OS-ELM for concept drift handling. Appl Soft Comput J 75:494–507. https://doi.org/10.1016/j.asoc.2018.11.006

    Article  Google Scholar 

  17. Dongre SS, Thomas A, Malik LG (2019) Detecting concept drift using HEDDM in data stream. Int J Intell Eng Inform 7(2/3):164. https://doi.org/10.1504/ijiei.2019.10020441

    Article  Google Scholar 

  18. Bifet A and Gavaldà R (2007) Learning from time-changing data with adaptive windowing. In: Proceedings of the 7th SIAM International Conference on Data Mining pp. 443–448. https://doi.org/10.1137/1.9781611972771.42

  19. Gomes HM, Read J and Bifet A (2019) Streaming Random Patches for Evolving Data Stream Classification. In: 2019 IEEE International Conference on Data Mining (ICDM), pp 240–249. https://doi.org/10.1109/ICDM.2019.00034

  20. Duda RO, Hart PE, and others (2006) Pattern classification. John Wiley \& Sons

  21. Damgaard CF, Irvine KM (2019) Using the beta distribution to analyse plant cover data. J Ecol 107(6):2747–2759. https://doi.org/10.1111/1365-2745.13200

    Article  Google Scholar 

  22. Yuan X, Chen C, Jiang M, Yuan Y (2019) Prediction interval of wind power using parameter optimised Beta distribution based LSTM model. Appl Soft Comput J 82:105550. https://doi.org/10.1016/j.asoc.2019.105550

    Article  Google Scholar 

  23. Althubyani FA, Abd El-Bar AMT, Fawzy MA, Gemeay AM (2022) A new 3-parameter bounded beta distribution: properties, estimation, and applications. Axioms. https://doi.org/10.3390/axioms11100504

    Article  Google Scholar 

  24. Santana-E-Silva JJ, Cribari-Neto F, Vasconcellos KLP (2022) Beta distribution misspecification tests with application to Covid-19 mortality rates in the United States. PLoS ONE. https://doi.org/10.1371/journal.pone.0274781

    Article  PubMed  PubMed Central  Google Scholar 

  25. Serinaldi F, Lombardo F (2020) Probability distribution of waiting time of the kth extreme event under serial dependence. J Hydrol Eng 25(6):1–11. https://doi.org/10.1061/(asce)he.1943-5584.0001923

    Article  Google Scholar 

  26. Skellam JG (1948) A probability distribution derived from the binomial distribution by regarding the probability of success as variable between the sets of trials. J Roy Stat Soc: Ser B (Methodol) 10(2):257–261. https://doi.org/10.1111/j.2517-6161.1948.tb00014.x

    Article  MathSciNet  Google Scholar 

  27. Han Y, Kim J, Ng HKT, Kim SW (2022) Logistic regression model for a bivariate binomial distribution with applications in baseball data analysis. Entropy 24(8):1–16. https://doi.org/10.3390/e24081138

    Article  MathSciNet  Google Scholar 

  28. Fleckenstein L, Kauschke S and Fürnkranz J (2019) Beta distribution drift detection for adaptive classifiers In: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp 443–448

  29. Beyene AA, Welemariam T, Persson M, Lavesson N (2015) Improved concept drift handling in surgery prediction and other applications. Knowl Inf Syst 44(1):177–196. https://doi.org/10.1007/s10115-014-0756-9

    Article  Google Scholar 

  30. Agrawal R, Swami A, Imielinski T (1993) Database mining: a performance perspective. IEEE Trans Knowl Data Eng 5(6):914–925. https://doi.org/10.1109/69.250074

    Article  Google Scholar 

  31. Elwell R, Polikar R (2011) Incremental learning of concept drift in nonstationary environments. IEEE Trans Neural Networks 22(10):1517–1531. https://doi.org/10.1109/TNN.2011.2160459

    Article  PubMed  Google Scholar 

  32. Dua D and Graff C (2017) {UCI} Machine Learning Repository, [Online]. Available: http://archive.ics.uci.edu/ml

  33. Rigatti SJ (2017) Random forest. J Insur Med 47(1):31–39. https://doi.org/10.17849/insm-47-01-31-39.1

    Article  PubMed  Google Scholar 

  34. Wang X, Kang Q, An J, Zhou M (2019) Drifted twitter spam classification using multiscale detection test on K-L divergence. IEEE Access 7:108384–108394. https://doi.org/10.1109/ACCESS.2019.2932018

    Article  Google Scholar 

  35. Kahraman A, Kantardzic M, Kotan M (2022) Dynamic modeling with integrated concept drift detection for predicting real-time energy consumption of industrial machines. IEEE Access 10:104622–104635. https://doi.org/10.1109/ACCESS.2022.3210525

    Article  Google Scholar 

  36. Lin L, Wen L, Lin L, Pei J, Yang H (2022) LCDD: detecting business process drifts based on local completeness. IEEE Trans Serv Comput 15(4):2086–2099. https://doi.org/10.1109/TSC.2020.3032787

    Article  Google Scholar 

  37. Yang Z, Al-Dahidi S, Baraldi P, Zio E, Montelatici L (2020) A novel concept drift detection method for incremental learning in nonstationary environments. IEEE Trans Neural Netw Learn Syst 31(1):309–320. https://doi.org/10.1109/TNNLS.2019.2900956

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia

    Ature Angbera & Huah Yong Chan

  2. Department of Computer Science, Joseph Sarwuan Tarka University, Makurdi, Nigeria

    Ature Angbera

Authors
  1. Ature Angbera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huah Yong Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Angbera Ature wrote the main manuscript text and conducted all the experiments and analysis, while H.Y. Chan supervised the work. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ature Angbera.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Angbera, A., Chan, H.Y. SABeDM: a sliding adaptive beta distribution model for concept drift detection in a dynamic environment. Knowl Inf Syst 66, 2039–2062 (2024). https://doi.org/10.1007/s10115-023-02004-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-023-02004-3

Keywords

Search

Navigation