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

  • Baoju ZhangEmail author
  • Yidi Chen
  • Lei Xue
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)


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.


Data stream Concept drift Fuzzy integral Ensemble classification 



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).


  1. 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)CrossRefGoogle Scholar
  2. 2.
    Peter, V., Abraham, B.: Entropy-based concept shift detection. In: International Conference on Data Mining, pp. 1113–1118. IEEE (2006)Google Scholar
  3. 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. 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. 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. 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

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina

Personalised recommendations