Probabilistic Hoeffding Trees

Sped-Up Convergence and Adaption of Online Trees on Changing Data Streams
  • Jonathan Boidol
  • Andreas Hapfelmeier
  • Volker Tresp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9165)

Abstract

Increasingly, data streams are generated from a growing number of small, cheap sensors that monitor, e.g., personal activities, industrial facilities or the natural environment. In these settings, there are often rapid changes in input-to-target relations and we are concerned with tree-structured models that can rapidly adapt to these changes. Based on our new algorithms accuracy and tracking behavior is improved, which we demonstrate for a number of popular tree based-classifiers with over state-of-the-art change detection using five data sets and two different settings. The key novel idea is the representation of record values as distributions rather than point-values in the stream setting, covering a larger part of the instance space early on, and resulting in an often smaller, more flexible classification model.

Keywords

Online decision tree learning Uncertainty-aware data streams Classification Concept change Regularization 

References

  1. 1.
    Aggarwal, C.C., Philip, S.Y.: Outlier detection with uncertain data. In: SDM, pp. 483–493. SIAM (2008)Google Scholar
  2. 2.
    Bifet, A., Gavaldà, R.: Adaptive learning from evolving data streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 249–260. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  3. 3.
    Bifet, A., Holmes, G., Kirkby, R., et al.: Moa: Massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)Google Scholar
  4. 4.
    Blackard, J.A., Dean, D.J.: Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Comput. Electron. Agric. 24(3), 131–151 (1999)CrossRefGoogle Scholar
  5. 5.
    Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Evaluating probabilistic queries over imprecise data. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 551–562. ACM (2003)Google Scholar
  6. 6.
    Cormode, G., McGregor, A.: Approximation algorithms for clustering uncertain data. In: Proceedings of the Twenty-Seventh ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 191–200. ACM (2008)Google Scholar
  7. 7.
    Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80. ACM (2000)Google Scholar
  8. 8.
    Freire, A.L., Barreto, G.A., Veloso, M., et al.: Short-term memory mechanisms in neural network learning of robot navigation tasks: A case study. In: 2009 6th Latin American Robotics Symposium (LARS), pp. 1–6. IEEE (2009)Google Scholar
  9. 9.
    Gama, J., Medas, P., Rodrigues, P.: Learning decision trees from dynamic data streams. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 573–577. ACM (2005)Google Scholar
  10. 10.
    Gama, J., Sebastião, R., Rodrigues, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2013)MATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Hang, Y., Fong, S.: Stream mining dynamic data by using iOVFDT. J. Emerg. Technol. Web Intell. 5(1), 78–86 (2013)Google Scholar
  12. 12.
    Hashemi, S., Yang, Y., Mirzamomen, Z., et al.: Adapted one-versus-all decision trees for data stream classification. IEEE Trans. Knowl. Data Eng. 21(5), 624–637 (2009)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Ikonomovska, E., Gama, J.: Learning model trees from data streams. In: Boulicaut, J.-F., Berthold, M.R., Horváth, T. (eds.) DS 2008. LNCS (LNAI), vol. 5255, pp. 52–63. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  15. 15.
    Kaluža, B., Mirchevska, V., Dovgan, E., Luštrek, M., Gams, M.: An agent-based approach to care in independent living. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 177–186. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  16. 16.
    Knuth, D.E.: The Art of Computer Programming. Seminumerical Algorithms, 3rd edn., vol. 2, p. 232. Addison-Wesley, Boston (1998)Google Scholar
  17. 17.
    Kriegel, H.P., Bernecker, T., Renz, M., et al.: Probabilistic Join Queries in Uncertain Databases (A Survey of Join Methods for uncertain data), vol. 35. Springer (2010)Google Scholar
  18. 18.
    Kriegel, H.P., Pfeifle, M.: Density-based clustering of uncertain data. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 672–677. ACM (2005)Google Scholar
  19. 19.
    Liang, C., Zhang, Y., Song, Q.: Decision tree for dynamic and uncertain data streams. In: ACML, pp. 209–224 (2010)Google Scholar
  20. 20.
    Ngai, W.K., Kao, B., Chui, C.K., et al.: Efficient clustering of uncertain data. In: Sixth International Conference on Data Mining, ICDM 2006, pp. 436–445. IEEE (2006)Google Scholar
  21. 21.
    Pfahringer, B., Holmes, G., Kirkby, R.: New options for hoeffding trees. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 90–99. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  22. 22.
    Qin, B., Xia, Y., Li, F.: DTU: a decision tree for uncertain data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 4–15. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  23. 23.
    Singh, S., Mayfield, C., Prabhakar, S., et al.: Indexing uncertain categorical data. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 616–625. IEEE (2007)Google Scholar
  24. 24.
    Stolfo, S.J., Fan, W., Lee, W., et al.: Cost-based modeling for fraud and intrusion detection: Results from the JAM project. In: Proceedings of the DARPA Information Survivability Conference and Exposition, DISCEX 2000, vol. 2, pp. 130–144. IEEE (2000)Google Scholar
  25. 25.
    Tsang, S., Kao, B., Yip, K.Y., et al.: Decision trees for uncertain data. IEEE Trans. Knowl. Data Eng. 23(1), 64–78 (2011)CrossRefGoogle Scholar
  26. 26.
    Wang, P., Wang, H., Wu, X., et al.: On reducing classifier granularity in mining concept-drifting data streams. In: Fifth IEEE International Conference on Data Mining, 8-p. IEEE (2005)Google Scholar
  27. 27.
    Yang, Y., Wu, X., Zhu, X.: Combining proactive and reactive predictions for data streams. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 710–715. ACM (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jonathan Boidol
    • 1
    • 2
  • Andreas Hapfelmeier
    • 2
  • Volker Tresp
    • 1
    • 2
  1. 1.Institute for Computer ScienceLudwig-Maximilians UniversityMünchenGermany
  2. 2.Siemens AG, Corporate TechnologyMünchenGermany

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