Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets

  • Authors:

  • Degang Chen;Wanlu Li;Xiao Zhang;Sam Kwong

  • Affiliations:

  • Department of Mathematics and Physics, North China Electric Power University, Beijing, 102206, PR China;Department of Mathematics and Physics, North China Electric Power University, Beijing, 102206, PR China;Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, PR China;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

Abstract

Covering rough sets generalize traditional rough sets by considering coverings of the universe instead of partitions, and neighborhood-covering rough sets have been demonstrated to be a reasonable selection for attribute reduction with covering rough sets. In this paper, numerical algorithms of attribute reduction with neighborhood-covering rough sets are developed by using evidence theory. We firstly employ belief and plausibility functions to measure lower and upper approximations in neighborhood-covering rough sets, and then, the attribute reductions of covering information systems and decision systems are characterized by these respective functions. The concepts of the significance and the relative significance of coverings are also developed to design algorithms for finding reducts. Based on these discussions, connections between neighborhood-covering rough sets and evidence theory are set up to establish a basic framework of numerical characterizations of attribute reduction with these sets.