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Neural Classification for Interval Information

  • Conference paper
Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8722))

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.

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References

  1. Alefeld, G., Hercberger, J.: Introduction to Interval Computations. Academic Press, New York (1986)

    Google Scholar 

  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. Bascil, M.S., Oztekin, H.: A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network. J. Med. Syst. 36, 1603–1606 (2012)

    Article  Google Scholar 

  4. Brandt, S.: Data Analysis. Springer, Heidelberg (1999)

    Book  MATH  Google Scholar 

  5. Jaulin, L., Kieffer, M., Didrit, O., Walter, E.: Applied Interval Analysis. Springer, Berlin (2001)

    Book  MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  7. Kotsiantis, S.B., Pintelas, P.E.: Logitboost of Simple Bayesian Classifier. Informatica 29, 53–59 (2005)

    Google Scholar 

  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. Kowalski, P.A.: Bayesian Classification of Imprecise Interval-Type Information (in Polish). SRI, Polish Academy of Sciences, Ph.D. Thesis (2009)

    Google Scholar 

  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. Kulczycki, P.: Statistical Inference for Fault Detection: A Complete Algorithm Based on Kernel Estimators. Kybernetika 38(2), 141–168 (2002)

    MATH  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  13. Kulczycki, P., Kowalski, P.A.: Bayes classification of imprecise information of interval type. Control and Cybernetics 40, 101–123 (2011)

    MathSciNet  Google Scholar 

  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)

    Chapter  Google Scholar 

  15. Moore, R.E.: Interval Analysis. Prentice-Hall, Englewood Cliffs (1966)

    MATH  Google Scholar 

  16. Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)

    Book  Google Scholar 

  17. Specht, D.F.: Probabilistic Neural Networks. Neural Networks 3, 109–118 (1990)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, London (1995)

    Book  MATH  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Department of Automatic Control and Information Technology, Cracow University of Technology, ul. Warszawska 24, PL-31-155, Cracow, Poland

    Piotr A. Kowalski & Piotr Kulczycki

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, PL-01-447, Warsaw, Poland

    Piotr A. Kowalski & Piotr Kulczycki

Authors
  1. Piotr A. Kowalski
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  2. Piotr Kulczycki
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Editor information

Editors and Affiliations

  1. Institute of Information and Communication, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

  2. Department of Computer Science, Wright State University, Dayton, OH, USA

    Pascal Hitzler

  3. Wright State University, Dayton, OH, USA

    Adila A. Krisnadhi

  4. Higher School of Economics, National Research University, Moscow, Russia

    Sergei O. Kuznetsov

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© 2014 Springer International Publishing Switzerland

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Kowalski, P.A., Kulczycki, P. (2014). Neural Classification for Interval Information. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2014. Lecture Notes in Computer Science(), vol 8722. Springer, Cham. https://doi.org/10.1007/978-3-319-10554-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-10554-3_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10553-6

  • Online ISBN: 978-3-319-10554-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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