Natural versus Granular Computing: Classifiers from Granular Structures

  • Piotr Artiemjew
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5306)

Abstract

In data sets/decision systems, written down as pairs (U,A ∪ {d}) with objects U, attributes A, and a decision d, objects are described in terms of attribute–value formulas. This representation gives rise to a calculus in terms of descriptors which we call a natural computing. In some recent papers, the idea of L. Polkowski of computing with granules induced from similarity measures called rough inclusions have been tested. In this work, we pursue this topic and we study granular structures resulting from rough inclusions with classification problem in focus. Our results show that classifiers obtained from granular structures give better quality of classification than natural exhaustive classifiers.

Keywords

rough inclusions granular computing classification of data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Piotr Artiemjew
    • 1
  1. 1.University of Warmia and MazuryOlsztynPoland

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