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Several Approaches to Attribute Reduction in Variable Precision Rough Set Model

  • Masahiro Inuiguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3558)

Abstract

In this paper, we discuss attribute reduction in variable precision rough set model. We consider several kinds of reducts preserving some of lower approximations, upper approximations, boundary regions and the unpredictable region. We show relations among those kinds of reducts. Moreover we discuss logical function representations of the preservation of lower approximations, upper approximations, boundary regions and the unpredictable region as a basis for reduct calculation. By those discussions, the great difference between the analysis using variable precision rough sets and the classical rough set analysis is emphasized.

Keywords

Logical Function Lower Approximation Decision Table Decision Class Discernibility Matrix 
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 2005

Authors and Affiliations

  • Masahiro Inuiguchi
    • 1
  1. 1.Department of Systems Innovation, Graduate School of Engineering ScienceOsaka UniversityOsakaJapan

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