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Efficient AdaBoost Region Classification

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5632))

Abstract

The task of region classification is to construct class regions containing the correct classes of the objects being classified with an error probability ε ∈ [0,1]. To turn a point classifier into a region classifier, the conformal framework is employed [11,14]. However, to apply the framework we need to design a non-conformity function. This function has to estimate the instance’s non-conformity for the point classifier used.

This paper introduces a new non-conformity function for AdaBoost. The function has two main advantages over the only existing non-conformity function for AdaBoost. First, it reduces the time complexity of computing class regions with a factor equal to the size of the training data. Second, it results in statistically better class regions.

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References

  1. Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)

    Google Scholar 

  2. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  3. Caprile, B., Furlanello, C., Merler, S.: Highlighting hard patterns via adaboost weights evolution. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS (LNAI), vol. 2364, pp. 72–80. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  4. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  5. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  6. Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)

    Article  MATH  Google Scholar 

  7. Niculescu-Mizil, A., Caruana, R.: Obtaining calibrated probabilities from boosting. In: Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence, Edinburgh, Scotland, July 26-29, pp. 413–418 (2005)

    Google Scholar 

  8. Proedrou, K.: Rigorous Measures of Confidence for Pattern Recognition and Regression. PhD thesis, Royal Holloway College, University of London, UK (2003)

    Google Scholar 

  9. Rätsch, G., Onoda, T., Müller, K.-R.: Soft margins for adaboost. Machine Learning 42(3), 287–320 (2001)

    Article  MATH  Google Scholar 

  10. Schapire, R.: The boosting approach to machine learning: An overview. In: MSRI Workshop on Nonlinear Estimation and Classification, Berkeley, CA (March 2001)

    Google Scholar 

  11. Shafer, G., Vovk, V.: A tutorial on conformal prediction. Journal of Machine Learning Research 9, 371–421 (2008)

    MathSciNet  MATH  Google Scholar 

  12. Smirnov, E.N., Vanderlooy, S., Sprinkhuizen-Kuyper, I.G.: Meta-typicalness approach to reliable classification. In: Proceedings of the 17th European Conference on Artificial Intelligence, Riva del Garda, Italy, August 28 - September 1, pp. 810–811. IOS Press, Amsterdam (2006)

    Google Scholar 

  13. Vanderlooy, S., van der Maaten, L., Sprinkhuizen-Kuyper, I.: Off-line learning with transductive confidence machines: an empirical evaluation. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 310–323. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  15. Zadrozny, B., Elkan, C.: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In: Proceedings of the 18th International Conference on Machine Learning, pp. 609–616. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Knowledge Engineering, Maastricht University, The Netherlands

    M. Moed & E. N. Smirnov

Authors
  1. M. Moed
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  2. E. N. Smirnov
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Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland, Germany

    Petra Perner

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© 2009 Springer-Verlag Berlin Heidelberg

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Moed, M., Smirnov, E.N. (2009). Efficient AdaBoost Region Classification. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_10

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  • DOI: https://doi.org/10.1007/978-3-642-03070-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03069-7

  • Online ISBN: 978-3-642-03070-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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