The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task

  • Andrzej Zolnierek
  • Bartlomiej Rubacha
Part of the Advances in Soft Computing book series (AINSC, volume 30)


In this paper the problems of sequential pattern recognition are considered. As a statistical model of dependence, in the sequences of patterns, the firstorder Markov chain is assumed. Additionally, the assumption about independence between the attributes in the feature vector is made. The pattern recognition algorithms with such assumption are called in the literature “naive Bayes algorithm”. In this paper such approach is made to the pattern recognition algorithm for first-order Markov chain and some results of numerical investigation are presented. The main goal of these investigations was to verify if it is reasonable to make such assumption in the real recognition tasks.


Markov Chain Feature Vector Simulation Investigation Pattern Recognition Algorithm Control Markov Chain 
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.
    Duda R, Hart P (1973) Pattern classification and scene analysis. John Wiley, New YorkzbMATHGoogle Scholar
  2. 2.
    Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classfiers. Machine learning 29:131–163zbMATHCrossRefGoogle Scholar
  3. 3.
    Fu K (1974) Syntactic methods in pattern recognition. New York Academic PressGoogle Scholar
  4. 4.
    Greblicki W (1978) Pattern recognition procedures with nonparametric density estimates. In: IEEE Trans. on SMC 8:809–812zbMATHMathSciNetGoogle Scholar
  5. 5.
    Kurzynski M (1997) Pattern recognition-statistical approach. Publishers of Wroclaw University of Technology.Google Scholar
  6. 6.
    Kurzynski M, Zolnierek A (1980) A recursive classifying decision rule for second-order Markov chain. Control and Cybernetics 9:141–147MathSciNetGoogle Scholar
  7. 7.
    McCallum, Nigam K (1998) A comparison of event models for naive Bayes text classication. In AAAI-98 Workshop on Learning and Text Categorization, Madison, WI, USA:41–48Google Scholar
  8. 8.
    Mitchel T (1997) Machine learning. McGraw Hill, New YorkGoogle Scholar
  9. 9.
    Raviv J (1967) Decision making in Markov chain applied to the problem of pattern recognition. In: IEEE Trans. on IT 21:536–551CrossRefGoogle Scholar
  10. 10.
    Zolnierek A (1982) Computer-aided recognition of the human acid-base state. In Proc. of 6-th Int. Conf. on Pattern Recognition:1219Google Scholar
  11. 11.
    Zolnierek A (1983) Pattern recognition algorithms for controlled Markov chains and their application to medical diagnosis. Pattern Recognition Letters 1:299–303CrossRefGoogle Scholar
  12. 12.
    Zolnierek A (2003) The simulation investigations of pattern recognition algorithm for second-order Markov chains. In: Proc. of the 37-th conference, Brno, Czech Republic, Acta MOSIS 92:29–35Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Andrzej Zolnierek
    • 1
  • Bartlomiej Rubacha
    • 1
  1. 1.Wroclaw University of TechnologyWroclawPoland

Personalised recommendations