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Discriminant Function Selection in Binary Classification Task

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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

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Abstract

The ensemble selection is one of the important problems in building multiple classifier systems (MCSs). This paper presents dynamic ensemble selection based on the analysis of discriminant functions. The idea of the selection is presented on the basis of binary classification tasks. The paper presents two approaches: one takes into account the normalization of the discrimination functions, and in the second approach, normalization is not performed. The reported results based on the data sets form the UCI repository show that the proposed ensemble selection is a promising method for the development of MCSs.

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References

  1. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)

    MATH  Google Scholar 

  2. Burduk, R.: Classifier fusion with interval-valued weights. Pattern Recognit. Lett. 34(14), 1623–1629 (2013)

    Article  Google Scholar 

  3. Britto, A.S., Sabourin, R., Oliveira, L.E.S.: Dynamic selection of classifiers—a comprehensive review. Pattern Recognit. 47(11), 3665–3680 (2014)

    Article  Google Scholar 

  4. Cyganek, B.: One-class support vector ensembles for image segmentation and classification. J. Math. Imaging Vis. 42(2–3), 103–117 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  5. Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic selection approaches for multiple classifier systems. Neural Comput. Appl. 22(3–4), 673–688 (2013)

    Article  Google Scholar 

  6. Cyganek, B., Woźniak, M.: Vehicle Logo Recognition with an Ensemble of Classifiers. Lecture Notes in Computer Science, vol. 8398, pp. 117–126. Springer, Berlin (2014)

    Google Scholar 

  7. Didaci, L., Giacinto, G., Roli, F., Marcialis, G.L.: A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognit. 38, 2188–2191 (2005)

    Article  MATH  Google Scholar 

  8. Frejlichowski, D.: An Algorithm for the Automatic Analysis of Characters Located on Car License Plates. Lecture Notes in Computer Science, vol. 7950, pp. 774–781. Springer, Berlin (2013)

    Google Scholar 

  9. Forczmański, P., Łabȩdź, P.: Recognition of Occluded Faces Based on Multi-subspace Classification. Lecture Notes in Computer Science, vol. 8104, pp. 148–157. Springer, Berlin (2013)

    Google Scholar 

  10. Frank, A., Asuncion, A.: UCI Machine Learning Repository (2010)

    Google Scholar 

  11. Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recognit. Lett. 22, 25–33 (2001)

    Article  MATH  Google Scholar 

  12. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  13. Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16(1), 66–75 (1994)

    Article  Google Scholar 

  14. Jackowski, K., Woźniak, M.: Method of classifier selection using the genetic approach. Expert Syst. 27(2), 114–128 (2010)

    Article  Google Scholar 

  15. Jackowski, K., Krawczyk, B., Woźniak, M.: Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning. Int. J. Neural Syst. 24(3) (2014)

    Google Scholar 

  16. Kuncheva, L.I.: A theoretical study on six classifier fusion strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 281–286 (2002)

    Article  Google Scholar 

  17. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Hoboken (2004)

    Book  MATH  Google Scholar 

  18. Kittler, J., Alkoot, F.M.: Sum versus vote fusion in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 110–115 (2003)

    Article  Google Scholar 

  19. Lam, L., Suen, C.Y.: Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans. Syst., Man, Cybern., Part A 27(5), 553–568 (1997)

    Article  Google Scholar 

  20. Przewoźniczek, M., Walkowiak, K., Woźniak, M.: Optimizing distributed computing systems for k-nearest neighbours classifiers-evolutionary approach. Logic J. IGPL 19(2), 357–372 (2010)

    Article  MathSciNet  Google Scholar 

  21. Rejer, I.: Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand. In: Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol. 226, pp. 581–590. Springer, Heidelberg (2013)

    Google Scholar 

  22. Ranawana, R., Palade, V.: Multi-classifier systems: review and a roadmap for developers. Int. J. Hybrid Intell. Syst. 3(1), 35–61 (2006)

    MATH  Google Scholar 

  23. Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63–81 (2005)

    Article  MATH  Google Scholar 

  24. Smȩtek, M., Trawiński, B.: Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms. New Gener. Comput. 29(3), 309–327 (2011)

    Article  Google Scholar 

  25. Suen, C.Y., Legault, R., Nadal, C.P., Cheriet, M., Lam, L.: Building a new generation of handwriting recognition systems. Pattern Recognit. Lett. 14(4), 303–315 (1993)

    Article  Google Scholar 

  26. Trawiński, K., Cordon, O., Quirin, A.: A study on the use of multiobjective genetic algorithms for classifier selection in furia-based fuzzy multiclassifiers. Int. J. Comput. Intell. Syst. 5(2), 231–253 (2012)

    Article  Google Scholar 

  27. Ulas, A., Semerci, M., Yildiz, O.T., Alpaydin, E.: Incremental construction of classifier and discriminant ensembles. Inf. Sci. 179(9), 1298–1318 (2009). Apr

    Article  Google Scholar 

  28. Woloszyński, T., Kurzyński, M.: A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recognit. 44(10–11), 2656–2668 (2011)

    Article  MATH  Google Scholar 

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Acknowledgments

This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264 and by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Technology.

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Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk

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  1. Robert Burduk
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Correspondence to Robert Burduk .

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Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Burduk, R. (2016). Discriminant Function Selection in Binary Classification Task. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26225-3

  • Online ISBN: 978-3-319-26227-7

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