Analysis of Diversity Assurance Methods for Combined Classifiers

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)


Assuring diversity of classifiers in an ensemble plays a crucial role in the multiple classifier system design. The paper presents a comparative study of selected methods which can assure the diversity by manipulating the individual classifier inputs i.e., they train learner using subspaces of a feature set or they try to exploit local competencies of individual classifier for a given subset of feature space. This work is a starting point for developing new methods of diversity assurance embedded in a multiple classifier system design. All methods had been evaluated on the basis of computer experiments which were carried out on benchmark datasets. On the basis of received results conclusions about the usefulness of examined methods for certain types of problems were drawn.


Feature Space Benchmark Dataset Ensemble Method Feature Selection Algorithm Random Subspace 
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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  1. 1.
    Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885–1892 (1999)CrossRefGoogle Scholar
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Cordella, L.P., Foggia, P., Sansone, C., Tortorella, F., Vento, M.: A Cascaded Multiple Expert System for Verification. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 330–339. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010),
  5. 5.
    Giacinto, G.: Design multiple classifier systems. Technical Report PhD thesis, Universita Degli Studi di Salerno, Salerno, Italy (1998)Google Scholar
  6. 6.
    Giacinto, G., Roli, F., Fumera, G.: Design of effective multiple classifier systems by clustering of classifiers. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 2, pp. 160–163 (2000)Google Scholar
  7. 7.
    Ho, K.T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)CrossRefGoogle Scholar
  8. 8.
    Holmes, G., Donkin, A., Witten, I.H.: Weka: A machine learning workbench. In: Proceedings of Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361 (1994)Google Scholar
  9. 9.
    Hornik, K., Buchta, C., Zeileis, A.: Open-source machine learning: R meets weka. Computational Statistics 24(2), 225–232 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Jackowski, K., Wozniak, M.: Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Analysis and Applications 12(4), 415–425 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264–323 (1999)CrossRefGoogle Scholar
  12. 12.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  13. 13.
    Krawczyk, B.: Classifier Committee Based on Feature Selection Method for Obstructive Nephropathy Diagnosis. In: Katarzyniak, R., Chiu, T.-F., Hong, C.-F., Nguyen, N.T. (eds.) Semantic Methods for Knowledge Management and Communication. SCI, vol. 381, pp. 115–125. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Kuncheva, L.I.: Clustering-and-selection model for classifier combination. In: Proceedings of Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 185–188 (2000)Google Scholar
  15. 15.
    Partridge, D., Krzanowski, W.: Software diversity: practical statistics for its measurement and exploitation. Information and Software Technology 39(10), 707–717 (1997)CrossRefGoogle Scholar
  16. 16.
    Rastrigin, L.A., Erenstein, R.H.: Method of Collective Recognition. Energoizdat, Moscow (1981)zbMATHGoogle Scholar
  17. 17.
    Rodríguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)CrossRefGoogle Scholar
  18. 18.
    Ruta, D., Gabrys, B.: Classifier selection for majority voting. Information Fusion 6(1), 63–81 (2005)CrossRefGoogle Scholar
  19. 19.
    R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)Google Scholar
  20. 20.
    Wolpert, D.H.: The supervised learning no-free-lunch theorems. In: Proc. 6th Online World Conference on Soft Computing in Industrial Applications, pp. 25–42 (2001)Google Scholar
  21. 21.
    Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Proceedings of Twentieth International Conference on Machine Learning, vol. 2, pp. 856–863 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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