Advertisement

Introduction

  • Moamar Sayed-MouchawehEmail author
Chapter
  • 726 Downloads
Part of the Studies in Big Data book series (SBD, volume 41)

Abstract

This introductory chapter intends to present the challenges related to the problem of learning from data streams in nonstationary environments. It focuses on the major challenges related to the learning with concept drift, learning with concept evolution, and learning with both concept drift and concept evolution. Then, it classifies the different methods and techniques of the state of the art that are used to address these challenges. This categorization is achieved according to how the learning is performed, how the data streams are processed, and how the changes are detected and integrated into the model. Finally, this chapter ends with a compact summary of the contents of this book by providing a paragraph about each of the contributions and how the learning process from data streams is performed (single learner or ensemble learners, centralized processing or distributed computing, classification, regression or clustering, window-based or sequential-based, applications targeted, etc.).

Keywords

Concept Drift Nonstationary Environments (NSE) Data Stream Clustering Method Dynamic Weighted Majority (DWM) ADWIN Bagging 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S.: Adaptive mobile activity recognition system with evolving data streams. Neurocomputing. 150, 304–317 (2015)CrossRefGoogle Scholar
  2. 2.
    Khamassi, I., Sayed-Mouchaweh, M., Hammami, M., Ghédira, K.: Self-adaptive windowing approach for handling complex concept drift. Cogn. Comput. 7(6), 772–790 (2015)CrossRefGoogle Scholar
  3. 3.
    Rashidi, P., Cook, D.J.: Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans. Syst. Man Cybern. Syst. Hum. 39(5), 949–959 (2009)CrossRefGoogle Scholar
  4. 4.
    Sayed-Mouchaweh, M.: Learning from Data Streams in Dynamic Environments. Springer, Cham (2016)CrossRefGoogle Scholar
  5. 5.
    Mouchaweh, M.S.: Diagnosis in real time for evolutionary processes in using pattern recognition and possibility theory. Int. J. Comput. Cogn. 2(1), 79–112 (2004)Google Scholar
  6. 6.
    Toubakh, H., Sayed-Mouchaweh, M.: Hybrid dynamic data-driven approach for drift-like fault detection in wind turbines. Evol. Syst. 6(2), 115–129 (2015)CrossRefGoogle Scholar
  7. 7.
    Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106. ACM, New York (2001)Google Scholar
  8. 8.
    Guha, S., Mishra, N.: Clustering data streams. In: Data Stream Management, pp. 169–187. Springer, Berlin (2016)CrossRefGoogle Scholar
  9. 9.
    Hartert, L., Sayed-Mouchaweh, M.: Dynamic supervised classification method for online monitoring in non-stationary environments. Neurocomputing. 126, 118–131 (2014)CrossRefGoogle Scholar
  10. 10.
    Mohamad, S., Bouchachia, A., Sayed-Mouchaweh, M.: A bi-criteria active learning algorithm for dynamic data streams. IEEE Trans. Neural Netw. Learn. Syst. 29, 74 (2018)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S.: Anynovel: detection of novel concepts in evolving data streams. Evol. Syst. 7(2), 73–93 (2016)CrossRefGoogle Scholar
  12. 12.
    Faria, E.R., Gonçalves, I.J., de Carvalho, A.C., Gama, J.: Novelty detection in data streams. Artif. Intell. Rev. 45(2), 235–269 (2016)CrossRefGoogle Scholar
  13. 13.
    Patcha, A., Park, J.M.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)CrossRefGoogle Scholar
  14. 14.
    Beringer, J., Hüllermeier, E.: Online clustering of parallel data streams. Data Knowl. Eng. 58(2), 180–204 (2006)CrossRefGoogle Scholar
  15. 15.
    Masud, M., Gao, J., Khan, L., Han, J., Thuraisingham, B.M.: Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans. Knowl. Data Eng. 23(6), 859–874 (2011)CrossRefGoogle Scholar
  16. 16.
    Mohamad, S., Sayed-Mouchaweh, M., Bouchachia, A.: Active learning for classifying data streams with unknown number of classes. Neural Netw. 98, 1–15 (2018)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute Mines-Telecom Lille DouaiDouaiFrance

Personalised recommendations