JACIII Vol.10 No.3 pp. 419-431
doi: 10.20965/jaciii.2006.p0419


Generic Database for Hybrid Bayesian Pattern Recognition

Kiril I. Tenekedjiev*, Carlos A. Kobashikawa**, Natalia D. Nikolova*,
and Kaoru Hirota**

*Department of Economics and Management, Technical University of Varna, 1 Studentska Str., 9010 Varna, Bulgaria

**Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

August 23, 2005
November 8, 2005
May 20, 2006
statistical pattern recognition, fuzzy pattern recognition, learning, prediction, database, MATLAB Tool
A Bayesian pattern recognition system is proposed, that processes information encoded by four types of features: discrete, pseudo-discrete, multi-normal continuous and independent continuous. This hybrid system utilizes the combined frequentist-subjective approach to probabilities, uses parametric and nonparametric techniques for the conditional likelihood estimation, and relies heavily on the fuzzy theory for data presentation, learning, and information fusion. The information for training, recognition, and prediction of the system is organized in a database, which is logically structured into three interconnected hierarchical sub-databases. A software tool is created under MATLAB that assures consistency, integrity, and maintenance of the database information. Three application examples from the fields of technical and medical diagnostics are presented, which illustrate the types of problems and levels of complexity that the database tool can handle.
Cite this article as:
K. Tenekedjiev, C. Kobashikawa, N. Nikolova, and K. Hirota, “Generic Database for Hybrid Bayesian Pattern Recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.3, pp. 419-431, 2006.
Data files:
  1. [1] Th. Bayes, “An Essay Towards Solving a Problem in the Doctrine of Chances,” Philosophical Transactions of the Royal Society, LII, pp. 370-418, 1763.
  2. [2] P. A. Devijver, and J. Kittler, “Pattern Recognition: A Statistical Approach,” Prentice-Hall, London, pp. 22-68, 1982.
  3. [3] R. O. Duda, and P. E. Hart, “Pattern Classification and Scene Analysis,” Second Edition, John Wiley & Sons, NY, pp. 84-91 & 161-164 & 215-216 & 282-284, 2001.
  4. [4] K. S. Fu, and T. Booth, “Grammatical Inference: Introduction and Survey – Part I,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, pp. 343-359, 1986.
  5. [5] K. S. Fu, “A Step Towards Unification of Syntactic and Statistical Pattern Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, pp. 398-404, 1986.
  6. [6] K. Fukunaga, “Introduction to Statistical Pattern Recognition,” Second Edition, Academic Press, NY, pp. 169-176, 1990.
  7. [7] K. Fukunaga, and R. P. Hayes, “Estimation of Classifier Performance,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, pp. 1087-1101, 1989.
  8. [8] J. B. Kadane, and L. J. Wolfson, “Experiences in Elicitation,” The Statistician, 47, pp. 3-19, 1998.
  9. [9] L. N. Kanal, B. A. Lambird, et al., “Structural Methods in Image Analysis and Recognition,” In P.R. Krishnaiah, L.N. Kanal (Eds.), “Handbook of Statistics, Volume 2 – Classification Pattern Recognition and Reduction of Dimensionality,” North-Holland, pp. 361-382, 1982.
  10. [10] A. K. Jain, R. P. Duin, et al., “Statistical Pattern Recognition: a Review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, pp. 4-37, 2000.
  11. [11] F. Hoti, and L. Holmstrom, “A Semi-Parametric Approach to Statistical Pattern Recognition,” Bulletin of the International Statistical Institute – Berlin, LX(1), pp. 510-511, 2003.
  12. [12] G. J. Klir, “Where Do We Stand on Measures of Uncertainty, Ambiguity, Fuzziness and the Like?,” Fuzzy Sets and Systems, 24, pp. 197-219, 1987.
  13. [13] G. J. Klir, and T. A. Folger, “Fuzzy Sets, Uncertainty and Information,” Prentice-Hall, Elglewood Cliffs, NJ, pp. 107-131, 1988.
  14. [14] A. O’Hagan, and J. E. Oakley, “Probability is Perfect, but We Can’t Elicit it Perfectly,” Reliability Engineering and System Safety, 85, pp. 239-248, 2004.
  15. [15] W. Pedrycz, “Fuzzy Sets in Pattern Recognition: Accomplishments and Challenges,” Fuzzy Sets and Systems, 90, pp. 171-176, 1997.
  16. [16] L. I. Perlovsky, “Conundrum of Combinatorial Complexity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, pp. 666-670, 1998.
  17. [17] M. Sugeno, “Fuzzy Measures and Fuzzy Integrals – a Survey,” In M. M. Gupta, G. N. Saridis et al. (Eds.), “Fuzzy Automata and Decision Processes,” North-Holland, NY, pp. 89-102, 1977.
  18. [18] K. Tenekedjiev, L. Altev, et al., “Main Ship Engines and Auxiliary Power Devices Technical Diagnostics Using Pseudo-Discrete Features,” Proc. First International Conference on Marine Industry MARIND’1996 – Varna, I, pp. 263-276, 1996.
  19. [19] L. A. Zadeh, “Fuzzy Sets and Their Application to Pattern Classification and Clustering Analysis,” In J. Van Ryzin (Ed.), “Classification and Clustering,” Academic Press, NY, pp. 251-299, 1977.
  20. [20] L. A. Zadeh, “Fuzzy Sets as a Basis for a Theory of Possibility,” Fuzzy Sets and Systems, 1, pp. 3-28, 1978.
  21. [21] K. I. Tenekedjiev, “Technical Diagnostics of Complex Energetic Objects Using Statistical Pattern Recognition,” Ph.D. thesis, Technical University – Varna, Bulgaria, pp. 90-96 & 98-108, 1994 (in Bulgarian).

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