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On a New Measure of Classifier Competence in the Feature Space

  • Tomasz Woloszynski
  • Marek Kurzynski
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

This paper presents a new method for calculating competence of a classifier in the feature space. The idea is based on relating the response of the classifier with the response obtained by a random guessing. The measure of competence reflects this relation and rates the classifier with respect to the random guessing in a continuous manner. Two multiclassifier systems representing fusion and selection strategies were developed using proposed measure of competence. The performance of multiclassifiers was evaluated using five benchmark databases from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. Classification results obtained for three simple fusion methods and one multiclassifier system with selection strategy were used for a comparison. The experimental results showed that, regardless of the strategy used by the multiclassifier system, the classification accuracy has increased when the measure of competence was employed.

Keywords

Feature Vector Feature Space Selection Strategy Class Label Fusion Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Asuncion, A., Newman, D.: UCI Machine Learning Repository. University of California, Department of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
  2. 2.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, Hoboken (2001)zbMATHGoogle Scholar
  3. 3.
    Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
  4. 4.
    Giacinto, G., Roli, F.: Design of effective neural network ensembles for image classification processes. Image Vision and Computing Journal 19, 699–707 (2001)CrossRefGoogle Scholar
  5. 5.
    Kuncheva, L.: Combining Pattern Classifiers. Wiley Interscience, Hoboken (2004)CrossRefzbMATHGoogle Scholar
  6. 6.
  7. 7.
    Rastrigin, L.A., Erenstein, R.H.: Method of Collective Recognition. Energoizdat, Moscow (1981)Google Scholar
  8. 8.
    Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 405–410 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tomasz Woloszynski
    • 1
  • Marek Kurzynski
    • 1
  1. 1.Faculty of Electronics, Chair of Systems and Computer NetworksTechnical University of WroclawWroclawPoland

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