Recognition of Fuzzy or Incompletely Described Objects

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)

Abstract

Typical pattern recognition problem consists in assigning of a given object (result of observation) to one of previously defined similarity classes of objects. The problem has an unique solution if the classes are disjoint; otherwise it may happen that the considered object can be assigned to a class only on a limited certainty level. A more general problem arises if the object being to be recognized has not been described with a full accuracy. The situations of uncertainty consisting in missing some components of objects description and in inaccuracy of some objects’ features or parameters description are considered. An approach to the solution of the ill-described objects recognition based on the concepts of relative logic is proposed. This makes the proposed approach closer to a natural human decision making supported by intuition and, as such, useful in the case of uncertainty concerning the input data of the recognition problem.

Keywords

Pattern recognition Limited input data Input data uncertainty Information variables Relative logic Semi-ordering 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesWarsawPoland

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