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On a New Measure of Classifier Competence in the Feature Space

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Computer Recognition Systems 3

Part of the book series: Advances in Intelligent and Soft Computing ((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.

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References

  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. Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, Hoboken (2001)

    MATH  Google Scholar 

  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. Giacinto, G., Roli, F.: Design of effective neural network ensembles for image classification processes. Image Vision and Computing Journal 19, 699–707 (2001)

    Article  Google Scholar 

  5. Kuncheva, L.: Combining Pattern Classifiers. Wiley Interscience, Hoboken (2004)

    Book  MATH  Google Scholar 

  6. Kuncheva, L.: Collection, http://www.informatics.bangor.ac.uk/~kuncheva/activities/real_data_full_set.htm

  7. Rastrigin, L.A., Erenstein, R.H.: Method of Collective Recognition. Energoizdat, Moscow (1981)

    Google Scholar 

  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)

    Article  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Woloszynski, T., Kurzynski, M. (2009). On a New Measure of Classifier Competence in the Feature Space. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_34

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  • DOI: https://doi.org/10.1007/978-3-540-93905-4_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-93904-7

  • Online ISBN: 978-3-540-93905-4

  • eBook Packages: EngineeringEngineering (R0)

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