Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks (original) (raw)

Abstract

This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.

Chapter PDF

Similar content being viewed by others

References

  1. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
    Chapter Google Scholar
  2. Duin, R.: The combining classifier: to train or not to train? In: Proceedings of the International Conference on Pattern Recognition ICPR 2002, Quebec (Canada), vol. II, pp. 765–770 (2002)
    Google Scholar
  3. Dietterich, T.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
    Chapter Google Scholar
  4. Kuncheva, L.I.: That elusive diversity in classifier ensembles. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 1126–1138. Springer, Heidelberg (2003)
    Chapter Google Scholar
  5. Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Machine Learning 51, 181–207 (2003)
    Article MATH Google Scholar
  6. Partridge, D., Griffith, N.: Multiple classifier systems: Software engineered, automatically modular leading to a taxonomic overview. Pattern Analysis and Applications 5, 180–188 (2002)
    Article MATH MathSciNet Google Scholar
  7. Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
    Google Scholar
  8. Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
    MATH Google Scholar
  9. Opitz, D., Shavlik, J.: Generating accurate and diverse members of a neural-network ensemble. In: Touretzky, D., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 535–541 (1996)
    Google Scholar
  10. Stamatatos, E., Widmer, G.: Music performer recognition using an ensemble of simple classifiers. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 335–339 (2002)
    Google Scholar
  11. Kuncheva, L.: Combining Pattern Classifiers: methods and algorithms. Wiley, Chichester (2004)
    Book MATH Google Scholar
  12. Ponce de León, P.J., Iñesta, J.M.: Statistical description models for melody analysis and characterization. In: Proceedings of the 2004 International Computer Music Conference, International Computer Music Association, pp. 149–156 (2004)
    Google Scholar
  13. Pérez-Sancho, C., Iñesta, J.M., Calera-Rubio, J.: Style recognition through statistical event models. In: Proceedings of the Sound and Music Computing Conference, SMC 2004 (2004)
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. Department of Software and Computing Systems, University of Alicante, P.O. box 99, E-03080, Alicante, Spain
    Francisco Moreno-Seco, José M. Iñesta, Pedro J. Ponce de León & Luisa Micó

Authors

  1. Francisco Moreno-Seco
  2. José M. Iñesta
  3. Pedro J. Ponce de León
  4. Luisa Micó

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology,
    Dit-Yan Yeung
  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
    James T. Kwok
  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
    Ana Fred
  4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
    Fabio Roli
  5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
    Dick de Ridder

Rights and permissions

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moreno-Seco, F., Iñesta, J.M., de León, P.J.P., Micó, L. (2006). Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_77

Download citation

Keywords

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.

Publish with us