Sequential classification via fuzzy relations

Authors:
Marek Kurzynski;Andrzej Zolnierek
Affiliations:
Faculty of Electronics, Chair of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland;Faculty of Electronics, Chair of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

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Abstract

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.