Advertisement

Abstract

The subject of the presented research is to determine the complete neural procedure for classifying inaccurate information, as given in the form of an interval vector. For such a formulated task, a basic functionality Probabilistic Neural Network was extended upon the interval type of information. As a consequence, a new type of neural network has been proposed. The presented methodology was positively verified using random and benchmark data sets. In addition, a comparative analysis of existing algorithms with similar conditions was made.

Keywords

neural networks probabilistic neural networks data analysis classification interval data imprecise information 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alefeld, G., Hercberger, J.: Introduction to Interval Computations. Academic Press, New York (1986)Google Scholar
  2. 2.
    Araghi, L.F., Khaloozade, H., Arvan, M.R.: Ship identification using probabilistic neural networks (PNN). In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 2, pp. 18–20 (2009)Google Scholar
  3. 3.
    Bascil, M.S., Oztekin, H.: A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network. J. Med. Syst. 36, 1603–1606 (2012)CrossRefGoogle Scholar
  4. 4.
    Brandt, S.: Data Analysis. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  5. 5.
    Jaulin, L., Kieffer, M., Didrit, O., Walter, E.: Applied Interval Analysis. Springer, Berlin (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kaytoue, M., Kuznetsov, S.O., Napoli, A., Duplessis, S.: Mining gene expression data with pattern structures in formal concept analysis. Information Science 181(10), 1989–2001 (2011)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Kotsiantis, S.B., Pintelas, P.E.: Logitboost of Simple Bayesian Classifier. Informatica 29, 53–59 (2005)Google Scholar
  8. 8.
    Kowalski, P.A., Lukasik, S., Charytanowicz, M., Kulczycki, P.: Data-Driven Fuzzy Modelling and Control with Kernel Density Based Clustering Technique. Polish Journal of Environmental Studies 17, 83–87 (2008)Google Scholar
  9. 9.
    Kowalski, P.A.: Bayesian Classification of Imprecise Interval-Type Information (in Polish). SRI, Polish Academy of Sciences, Ph.D. Thesis (2009)Google Scholar
  10. 10.
    Kowalski, P.A., Kulczycki, P.: Data Sample Reduction for Classification of Interval Information Using Neural Network Sensitivity Analysis. In: Dicheva, D., Dochev, D. (eds.) AIMSA 2010. LNCS (LNAI), vol. 6304, pp. 271–272. Springer, Heidelberg (2010)Google Scholar
  11. 11.
    Kulczycki, P.: Statistical Inference for Fault Detection: A Complete Algorithm Based on Kernel Estimators. Kybernetika 38(2), 141–168 (2002)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Kulczycki, P., Charytanowicz, M., Kowalski, P.A., Lukasik, S.: The Complete Gradient Clustering Algorithm: properties in practical applications. Journal of Applied Statistics 39(6), 1211–1224 (2012)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Kulczycki, P., Kowalski, P.A.: Bayes classification of imprecise information of interval type. Control and Cybernetics 40, 101–123 (2011)MathSciNetGoogle Scholar
  14. 14.
    Kusy, M., Kluska, J.: Probabilistic Neural Network Structure Reduction for Medical Data Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 118–129. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Moore, R.E.: Interval Analysis. Prentice-Hall, Englewood Cliffs (1966)zbMATHGoogle Scholar
  16. 16.
    Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)CrossRefGoogle Scholar
  17. 17.
    Specht, D.F.: Probabilistic Neural Networks. Neural Networks 3, 109–118 (1990)CrossRefGoogle Scholar
  18. 18.
    Tran, T., Nguyen, T., Tsai, P., Kong, X.: BSPNN: boosted subspace probabilistic neural network for email security. Artif. Intell. Rev. 35, 369–382 (2011)CrossRefGoogle Scholar
  19. 19.
    Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, London (1995)CrossRefzbMATHGoogle Scholar
  20. 20.
    Zhao, Y., He, Q., Chen, Q.: An Interval Set Classification Based on Support Vector Machines. In: 2nd International Conference on Networking and Services, Silicon Valley, pp. 81–86 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Piotr A. Kowalski
    • 1
    • 2
  • Piotr Kulczycki
    • 1
    • 2
  1. 1.Department of Automatic Control and Information TechnologyCracow University of TechnologyCracowPoland
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland

Personalised recommendations