Sequential Classification Via Fuzzy Relations

  • Marek Kurzynski
  • Andrzej Zolnierek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


In this paper there are developed and evaluated methods for performing sequential classification (SC) using fuzzy relations defined on product of class set and fuzzified feature space. First on the base of learning set, fuzzy relation in the proposed method is determined as a solution of appropriate optimization problem. Next, this relation in the form of matrix of membership degrees is used at successive instants of sequential decision process. Three various algorithms of SC which differ both in the sets of input data and procedure are described. Proposed algorithms were practically applied to the computer-aided recognition of patient’s acid-base equilibrium states where as an optimization procedure the real-coded genetic algorithm (RGA) was used.


Feature Space Membership Degree Fuzzy Relation Fuzzy Relation Equation Sequential Decision Process 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marek Kurzynski
    • 1
  • Andrzej Zolnierek
    • 1
  1. 1.Faculty of Electronics, Chair of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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