Skip to main content

Clustering Data Manipulation Method for Ensembles

  • Conference paper
AI 2006: Advances in Artificial Intelligence (AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4304))

Included in the following conference series:

  • 3677 Accesses

Abstract

Traditional data manipulation models such as Bagging and Boosting select training cases from throughout the problem space to generate diversity and improve performance. A new data manipulation model is proposed that dynamically assigns specialists to train on difficult clusters of training data. The model allows the expertise of specialists to overlap for difficult regions of the problem. It has been coupled with a dynamic combination model to exploit the diversity of specialist members. The model has been applied to an environmental problem and has demonstrated that dynamic modelling can enhance both the diversity of members and the accuracy of the ensemble.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Kuncheva, L.: That elusive diversity in classifier ensembles. In: IbPRIA. First Iberian Conference on Pattern Recognition and Image Analysis, pp. 1126–1138 (2003)

    Google Scholar 

  2. McQueen, J.: Some methods of classification and analysis of multivariate observations. In: 5th Berkeley Symp. on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  3. Cover, T., Hart, P.: Nearest neighbour pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)

    Article  MATH  Google Scholar 

  4. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  5. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Machine Learning. Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

  6. Jacobs, R., Jordan, M., Nowlan, S., Hinton, G.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)

    Article  Google Scholar 

  7. Spencer, M., Whitfort, T., McCullagh, J., Bui, E.: Dynamic ensemble approach for estimating organic carbon using computational intelligence. In: ACST 2006 (2006)

    Google Scholar 

  8. Climate Change Science Program: Strategic plan for the U.S. climate change science program. Technical report (2003)

    Google Scholar 

  9. Johnston, R., Barry, S., Bleys, E., Bui, E., Moran, C., Simon, D., Carlile, P., McKenzie, N., Henderson, B., Chapman, G., Imhoff, M., Maschmedt, D., Howe, D., Grose, C., Schoknecht, N., Powell, B., Grundy, M.: ASRIS: The Database. Australian Journal of Soil Research 41, 1021–1036 (2003)

    Article  Google Scholar 

  10. Henderson, B., Bui, E., Moran, C., Simon, D.: Australia-wide predictions of soil properties using decision trees. Geoderma 124, 383–398 (2004)

    Article  Google Scholar 

  11. Quinlan, J.: Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific, Singapore (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Spencer, M., McCullagh, J., Whitfort, T. (2006). Clustering Data Manipulation Method for Ensembles. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_134

Download citation

  • DOI: https://doi.org/10.1007/11941439_134

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49787-5

  • Online ISBN: 978-3-540-49788-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics