Advertisement

Ensemble of Classifiers with Modification of Confidence Values

  • Robert BurdukEmail author
  • Paulina Baczyńska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9842)

Abstract

In the classification task, the ensemble of classifiers have attracted more and more attention in pattern recognition communities. Generally, ensemble methods have the potential to significantly improve the prediction base classifier which are included in the team. In this paper, we propose the algorithm which modifies the confidence values. This values are obtained as an outputs of the base classifiers. The experiment results based on thirteen data sets show that the proposed method is a promising method for the development of multiple classifiers systems. We compared the proposed method with other known ensemble of classifiers and with all base classifiers.

Keywords

Multiple classifier system Decision profile Confidence value 

Notes

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.

References

  1. 1.
    Alcalá, J., Fernández, A., Luengo, J., Derrac, J., GarcÍa, S., Sánchez, L., Herrera, F.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult.-Valued Logic Soft Comput. 17(2–3), 255–287 (2010)Google Scholar
  2. 2.
    Baczyńska, P., Burduk, R.: Ensemble selection based on discriminant functions in binary classification task. In: Jackowski, K., et al. (eds.) IDEAL 2015. LNCS, vol. 9375, pp. 61–68. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24834-9_8CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Randomizing outputs to increase prediction accuracy. Mach. Learn. 40(3), 229–242 (2000)zbMATHCrossRefGoogle Scholar
  4. 4.
    Britto, A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiers–a comprehensive review. Pattern Recogn. 47(11), 3665–3680 (2014)CrossRefGoogle Scholar
  5. 5.
    Burduk, R.: Classifier fusion with interval-valued weights. Pattern Recogn. Lett. 34(14), 1623–1629 (2013)CrossRefGoogle Scholar
  6. 6.
    Canuto, A.M., Abreu, M.C., de Melo Oliveira, L., Xavier, J.C., Santos, A.D.M.: Investigating the inuence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recogn. Lett. 28(4), 472–486 (2007)CrossRefGoogle Scholar
  7. 7.
    Duin, R.P.: The combining classifier: to train or not to train? In: Proceedings of the 16th International Conference on Pattern Recognition, 2002, vol. 2, pp. 765–770. IEEE (2002)Google Scholar
  8. 8.
    Forczmański, P., Łabedź, P.: Recognition of occluded faces based on multi-subspace classification. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 148–157. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository (2010)Google Scholar
  10. 10.
    Frejlichowski, D.: An algorithm for the automatic analysis of characters located on car license plates. In: Kamel, M., Campilho, A. (eds.) ICIAR 2013. LNCS, vol. 7950, pp. 774–781. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148–156 (1996)Google Scholar
  12. 12.
    Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recogn. Lett. 22, 25–33 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Inbarani, H.H., Azar, A.T., Jothi, G.: Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput. Methods Programs Biomed. 113(1), 175–185 (2014)CrossRefGoogle Scholar
  14. 14.
    Jackowski, K., Krawczyk, B., Woźniak, M.: Improved adaptive splitting, selection: the hybrid training method of a classifier based on a feature space partitioning. Int. J. Neural Syst. 24(03), 1430007 (2014)CrossRefGoogle Scholar
  15. 15.
    Korytkowski, M., Rutkowski, L., Scherer, R.: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Hoboken (2004)zbMATHCrossRefGoogle Scholar
  17. 17.
    Kuncheva, L.I., Bezdek, J.C., Duin, R.P.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn. 34(2), 299–314 (2001)zbMATHCrossRefGoogle Scholar
  18. 18.
    Rejer, I.: Genetic algorithm with aggressive mutation for feature selection in BCI feature space. Pattern Anal. Appl. 18(3), 485–492 (2015)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63–81 (2005)zbMATHCrossRefGoogle Scholar
  20. 20.
    Trawiński, B., Smȩetek, M., Telec, Z., Lasota, T.: Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. Int. J. Appl. Math. Comput. Sci. 22(4), 867–881 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Xu, L., Krzyżak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)CrossRefGoogle Scholar
  22. 22.
    Zdunek, R., Nowak, M., Pliński, E.: Statistical classification of soft solder alloys by laser-induced breakdown spectroscopy: review of methods. J. Eur. Optical Soc.-Rapid Publ. 11(16006), 1–20 (2016)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Personalised recommendations