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A Pattern-Based Bayesian Classifier for Data Stream

  • Jidong Yuan
  • Zhihai Wang
  • Yange Sun
  • Wei Zhang
  • Jingjing Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

An advanced approach to Bayesian classification is based on exploited patterns. However, traditional pattern-based Bayesian classifiers cannot adapt to the evolving data stream environment. For that, an effective Pattern-based Bayesian classifier for Data Stream (PBDS) is proposed. First, a data-driven lazy learning strategy is employed to discover local frequent patterns for each test record. Furthermore, we propose a summary data structure for compact representation of data, and to find patterns more efficiently for each class. Greedy search and minimum description length combined with Bayesian network are applied to evaluating extracted patterns. Experimental studies on real-world and synthetic data streams show that PBDS outperforms most state-of-the-art data stream classifiers.

Keywords

Data stream Frequent pattern Bayesian Lazy learning 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China (Nos. 61672086 and 61702030) and the Fundamental Research Funds for the Central Universities (Nos. 2016RC048 and 2016YJS036).

References

  1. 1.
    Baralis, E., Cagliero, L., Garza, P.: Enbay: a novel pattern-based bayesian classifier. IEEE Trans. Knowl. Data Eng. 25(12), 2780–2795 (2013)CrossRefGoogle Scholar
  2. 2.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11(May), 1601–1604 (2010)Google Scholar
  3. 3.
    Bifet, A., Pfahringer, B., Read, J., Holmes, G.: Efficient data stream classification via probabilistic adaptive windows. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 801–806. ACM (2013)Google Scholar
  4. 4.
    Cheng, H., Yan, X., Han, J., Hsu, C.W.: Discriminative frequent pattern analysis for effective classification. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 716–725. IEEE (2007)Google Scholar
  5. 5.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Machine Learning, pp. 1022–1027 (1993)Google Scholar
  6. 6.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)CrossRefMATHGoogle Scholar
  7. 7.
    Gama, J., Kosina, P., et al.: Learning decision rules from data streams. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p. 1255 (2011)Google Scholar
  8. 8.
    Gomes, H.M., Barddal, J.P., Enembreck, F., Bifet, A.: A survey on ensemble learning for data stream classification. ACM Comput. Surv. (CSUR) 50(2), 23 (2017)CrossRefGoogle Scholar
  9. 9.
    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 (2001)Google Scholar
  10. 10.
    Li, H.F., Shan, M.K., Lee, S.Y.: DSM-FI: an efficient algorithm for mining frequent itemsets in data streams. Knowl. Inf. Syst. 17(1), 79–97 (2008)CrossRefGoogle Scholar
  11. 11.
    Meretakis, D., Wüthrich, B.: Extending Naive Bayes classifiers using long itemsets. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 165–174. ACM (1999)Google Scholar
  12. 12.
    Sun, Y., Wang, Z., Liu, H., Du, C., Yuan, J.: Online ensemble using adaptive windowing for data streams with concept drift. Int. J. Distrib. Sens. Netw. 12, 4218973 (2016)CrossRefGoogle Scholar
  13. 13.
    Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM (2003)Google Scholar
  14. 14.
    Yuan, J., Wang, Z., Han, M., Sun, Y.: A lazy associative classifier for time series. Intell. Data Anal. 19(5), 983–1002 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jidong Yuan
    • 1
  • Zhihai Wang
    • 1
  • Yange Sun
    • 1
  • Wei Zhang
    • 1
  • Jingjing Jiang
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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