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

Degang CHEN, Wanlu LI, Xiao ZHANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

74 Citations (Scopus)

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. © 2013 Elsevier Inc.
Original languageEnglish
Pages (from-to)908-923
JournalInternational Journal of Approximate Reasoning
Volume55
Issue number3
Early online date4 Nov 2013
DOIs
Publication statusPublished - Mar 2014
Externally publishedYes

Bibliographical note

This paper is supported by the grants of NSFC ( 71171080 and 61170107 ) and the Co-construction Project of the Beijing Municipal Commission of Education .

Keywords

  • Attribute reduction
  • Belief and plausibility functions
  • Covering rough sets
  • Evidence theory
  • Neighborhood
  • Rough sets

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