Advertisement

Fuzzy Gaussian Process Classification Model

  • Eman Ahmed
  • Neamat El Gayar
  • Amir F. Atiya
  • Iman A. El Azab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5627)

Abstract

Soft labels allow a pattern to belong to multiple classes with different degrees. In many real world applications the association of a pattern to multiple classes is more realistic; to describe overlap and uncertainties in class belongingness. The objective of this work is to develop a fuzzy Gaussian process model for classification of soft labeled data. Gaussian process models have gained popularity in the recent years in classification and regression problems and are example of a flexible, probabilistic, non-parametric model with uncertainty predictions. Here we derive a fuzzy Gaussian model for a two class problem and then explain how this can be extended to multiple classes. The derived model is tested on different fuzzified datasets to show that it can adopt to various classification problems. Results reveal that our model outperforms the fuzzy K-Nearest Neighbor (FKNN), applied on the fuzzified dataset, as well as the Gaussian process and the K-Nearest Neighbor models used with crisp labels.

Keywords

Fuzzy Classification Gaussian Process(es) Soft labels 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    El Gayar, N., Schwenker, F., Palm, G.: A study of the robustness of knn classifiers trained using soft labels. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS, vol. 4087, pp. 67–80. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Pal, S., Mitra, S.: Multilayer perceptron, fuzzy sets and classification. IEEE Transactions on Neural Networks 3, 683–697 (1992)CrossRefGoogle Scholar
  3. 3.
    El Gayar, N.: Fuzzy Neural Network Models for Unsupervised and Confidence-Based Learning. PhD thesis, Dept. of Comp. Sc., University of Alexandria (1999)Google Scholar
  4. 4.
    Keller, J., Gray, M., Givens, J.: A fuzzy k-nearest algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 693–699 (1985)CrossRefGoogle Scholar
  5. 5.
    Kuncheva, L.: Fuzzy classifier design. Physica-verlag (2000)Google Scholar
  6. 6.
    Lin, C., Wang, S.: Fuzzy support vector machines. IEEE Transactions on Neural Networks 13, 464–471 (2002)CrossRefGoogle Scholar
  7. 7.
    Borasca, B., Bruzzone, L., Carlin, L., Zusi, M.: A fuzzy-input fuzzy-output svm technique for classification of hyperspectral remote sensing images. In: NORSIG 2006, Reykjavk (2006)Google Scholar
  8. 8.
    Thiel, C., Scherer, S., Schwenker, F.: Fuzzy-input fuzzy-output one-against-all support vector machines. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part III. LNCS, vol. 4694, pp. 156–165. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Seo, S., Obermayer, K.: Soft learning vector quantization. Neural Computation 15, 1589–1604 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Villmann, T., Hammer, B., Schleif, F., Geweniger, T.: Fuzzy labeled neural gas for fuzzy classification. In: WSOM 2005, Paris, France, September 2005, pp. 283–290 (2005)Google Scholar
  11. 11.
    Villmann, T., Schleif, F., Hammer, B.: Fuzzy labeled soft nearest neighbor classification with relevance learning. In: ICMLA 2005, December 2005, pp. 11–15. IEEE Press, Los Angeles, USA (2005)Google Scholar
  12. 12.
    Thiel, C., Sonntag, B., Schwenker, F.: Experiments with supervised fuzzy lvq. In: Prevost, L., Marinai, S., Schwenker, F. (eds.) ANNPR 2008. LNCS, vol. 5064, pp. 125–132. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Nickisch, H., Rasmussen, C.: Approximations for binary gaussian process classification. Journal of Machine Learning Research 9, 2035–2078 (2008)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Rasmussen, C., Williamsm, C.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)Google Scholar
  15. 15.
    Fisher, R.: The use of multiple measurements in taxonomic problems. Annual Eugenics 7, Part II, 179–188 (1936)Google Scholar
  16. 16.
    Murphy, P., Aha, D.: UCI repository of machine learning databases. PhD thesis, University of California, Dept. of Information and Computer Science, Irvine, CA (1992)Google Scholar
  17. 17.
    Merz, J., Murphy, P.: UCI repository of machine learning databases (1996), http://www.ics.uci.edu/learn/MLRepository.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Eman Ahmed
    • 1
  • Neamat El Gayar
    • 1
    • 2
  • Amir F. Atiya
    • 3
  • Iman A. El Azab
    • 1
  1. 1.Faculty of Computers and InformationCairo UniversityGizaEgypt
  2. 2.Centre of Informatics Science, School of Communication and Information TechnologyNile UniversityGizaEgypt
  3. 3.Faculty of EngineeringCairo UniversityGizaEgypt

Personalised recommendations