Distance: A more comprehensible perspective for measures in rough set theory

  • Authors:

  • Jiye Liang;Ru Li;Yuhua Qian

  • Affiliations:

  • School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China and Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of E ...;School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China and Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of E ...;School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China and Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of E ...

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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Abstract

Distance provides a comprehensible perspective for characterizing the difference between two objects in a metric space. There are many measures which have been proposed and applied for various targets in rough set theory. In this study, through introducing set distance and partition distance to rough set theory, we investigate how to understand measures from rough set theory in the viewpoint of distance, which are inclusion degree, accuracy measure, rough measure, approximation quality, fuzziness measure, three decision evaluation criteria, information measure and information granularity. Moreover, a rough set framework based on the set distance is also a very interesting perspective for understanding rough set approximation. From the view of distance, these results look forward to providing a more comprehensible perspective for measures in rough set theory.