Skip to main content
SpringerLink
Log in

Probabilistic Hoeffding Trees

Sped-Up Convergence and Adaption of Online Trees on Changing Data Streams

  • Conference paper
  • First Online:
Advances in Data Mining: Applications and Theoretical Aspects (ICDM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9165))

Included in the following conference series:

  • 1447 Accesses

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aggarwal, C.C., Philip, S.Y.: Outlier detection with uncertain data. In: SDM, pp. 483–493. SIAM (2008)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  3. Bifet, A., Holmes, G., Kirkby, R., et al.: Moa: Massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. Gama, J., Sebastião, R., Rodrigues, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  16. Knuth, D.E.: The Art of Computer Programming. Seminumerical Algorithms, 3rd edn., vol. 2, p. 232. Addison-Wesley, Boston (1998)

    Google Scholar 

  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. 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. Liang, C., Zhang, Y., Song, Q.: Decision tree for dynamic and uncertain data streams. In: ACML, pp. 209–224 (2010)

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. Tsang, S., Kao, B., Yip, K.Y., et al.: Decision trees for uncertain data. IEEE Trans. Knowl. Data Eng. 23(1), 64–78 (2011)

    Article  Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

  1. Institute for Computer Science, Ludwig-Maximilians University, Oettingenstr. 67, 80538, München, Germany

    Jonathan Boidol & Volker Tresp

  2. Siemens AG, Corporate Technology, Otto-Hahn-Ring 6, 81739, München, Germany

    Jonathan Boidol, Andreas Hapfelmeier & Volker Tresp

Authors
  1. Jonathan Boidol
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Hapfelmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Volker Tresp
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonathan Boidol .

Editor information

Editors and Affiliations

  1. IBaI, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Boidol, J., Hapfelmeier, A., Tresp, V. (2015). Probabilistic Hoeffding Trees. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20910-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20909-8

  • Online ISBN: 978-3-319-20910-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation