Fuzzy Integral Combination of One-Class Classifiers Designed for Multi-class Classification

  • Bilal HadjadjiEmail author
  • Youcef Chibani
  • Hassiba Nemmour
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)


One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its main advantage for multi-class is offering an open system and therefore allows easily extending new classes without retraining OCCs. Generally, pattern recognition systems designed by a single source of information suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining information from multiple sources becomes a mode for designing pattern recognition systems. Usually, fixed rules such as average, product, minimum and maximum are the standard used combiners for OCC ensembles. However, fixed combiners cannot be useful to treat some difficult cases. Hence, we propose in this paper a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Experimental results conducted on different types of OCC and two different handwritten datasets prove the superiority of FI against fixed combiners for an open multi-class classification based on OCC ensemble.


Combination Scheme Fuzzy Measure Speaker Verification Handwritten Digit MultiClass Classification 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tax, D.M.J.: One-class classification, PhD Thesis, Delft University of Technology (2001), ISBN: 90-75691-05-xGoogle Scholar
  2. 2.
    Kwang-Kyu, S.: An application of one-class support vector machines in content-based image retrieval. Expert System with Applications 33(2), 491–498 (2007)CrossRefGoogle Scholar
  3. 3.
    Manevitz, L., Yousef, M.: One-class document classification via Neural Networks. Neurocomputing 70, 1466–1481 (2007)CrossRefGoogle Scholar
  4. 4.
    Bergani, C., Oliveira, L.S., Koreich, A.L., Sabourin, R.: Combining different biometric traits with one-class classification. Signal Processing 89, 2117–2127 (2009)CrossRefGoogle Scholar
  5. 5.
    Sun, B.-Y., Huang, D.-S.: Support vector clustering for multi-class classification problems. In: The Congress on Evolutionary Computation, Canberra, Australia, pp. 1480–1485Google Scholar
  6. 6.
    Yeh, C.Y., Lee, Z.Y., Lee, S.J.: Boosting one-class support vector machines for multi-class classification. Applied Artificial Intelligence 23(4), 297–315 (2009)CrossRefGoogle Scholar
  7. 7.
    Boehm, O., Hardoon, D.R., Manevitz, L.M.: Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. International Journal of Machine Learning & Cyber 2, 125–134 (2011)CrossRefGoogle Scholar
  8. 8.
    Chiang, J.H., Gaber, P.D.: Hybrid fuzzy-neural systems in handwritten word recognition. IEEE Trans. Fuzzy Syst. 5, 497–510 (1997)CrossRefGoogle Scholar
  9. 9.
    Pham, T., Wagner, M.: Similarity normalization for speaker verification by fuzzy fusion. Pattern Recognit. 33, 309–315 (2000)CrossRefGoogle Scholar
  10. 10.
    Chiang, J.H.: Choquet fuzzy integral-based hierarchical networks for decision analysis. IEEE Trans. Fuzzy Syst. 7, 63–71 (1999)CrossRefGoogle Scholar
  11. 11.
    Cabrera, J.B.D., Gutiérrez, C., Mehra, R.K.: Ensemble methods for anomaly detection and distributed intrusion detection in Mobile Ad-Hoc Networks. Information Fusion 9, 96–119 (2008)CrossRefGoogle Scholar
  12. 12.
    Wilk, T., Wozniak, M.: Soft computing methods applied to combination of one-class classifiers. Neurocomputing 75, 185–193 (2012)CrossRefGoogle Scholar
  13. 13.
    Juszczak, P., Duin, R.P.: Combining One-Class Classifiers to Classify Missing Data. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 92–101. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Muñoz-Marí, J., Camps-Valls G., Gómez-Chova, L., Calpe-Maravilla, J.: Combination of one class remote sensing image classifiers. In: IGARSS, pp. 1509–1512 (2007)Google Scholar
  15. 15.
    Abbas, N., Chibani, Y., Belhadi, Z., Hedir, M.: A DSmT Based Combination Scheme for MultiClass Classification. In: 16th International Conference on Information FUSION: ICIF 2013, Instanbul, Turkey, July 9–12 (2013)Google Scholar
  16. 16.
    Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience Publication, New Jersey (2004)CrossRefGoogle Scholar
  17. 17.
    Cho, S.-B., Kim, J.H.: Combining multiple neural networks by fuzzy integrals for robust classification. IEEE Trans. Syst. Man Cybern. 25(2), 380–384 (1995)CrossRefGoogle Scholar
  18. 18.
    Cho, S.-B.: Fuzzy aggregation of modular neural networks with ordered weighted averaging operators. International Journal of Approximate Reasoning 13(4), 359–375 (1995)CrossRefzbMATHGoogle Scholar
  19. 19.
    Cho, S.-B.: Fusion of neural networks with fuzzy logic and genetic algorithm. Integrated Computer-Aided Engineering 9(4), 363–372 (2002)Google Scholar
  20. 20.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley & Sons, NY (2001)zbMATHGoogle Scholar
  21. 21.
  22. 22.
    Candès, E., Donoho, D.L.: New tight frames of curvelets and optimal representations of objects with piecewise singularities. Comm. Pure Appl. Math 57, 219–266 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Shirdhonkar, M.S., Kokare, M.: Off-Line Handwritten Signature Retrieval using Curvelet Transforms. International Journal of Computer and Engineering 3(4), 1658–1665 (2011)Google Scholar
  24. 24.
    Duin, R.P.W.: The combining classifier: to train or not to train?. In: Proc. 16th International Conference on Pattern Recognition, ICPR 2002, Canada, pp. 765–770 (2002)Google Scholar
  25. 25.
  26. 26.
    Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.: Estimating the support of a high dimensional distribution. Neural Computation 13(7), 1443–1472 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bilal Hadjadji
    • 1
    Email author
  • Youcef Chibani
    • 1
  • Hassiba Nemmour
    • 1
  1. 1.Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer ScienceUniversity of Science and Technology Houari Boumediene (USTHB)AlgiersAlgeria

Personalised recommendations