Sample pair selection for attribute reduction with rough set

Degang Chen, Suyun Zhao, Lei Zhang, Yongping Yang, Xiao Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

104 Citations (Scopus)


Attribute reduction is the strongest and most characteristic result in rough set theory to distinguish itself to other theories. In the framework of rough set, an approach of discernibility matrix and function is the theoretical foundation of finding reducts. In this paper, sample pair selection with rough set is proposed in order to compress the discernibility function of a decision table so that only minimal elements in the discernibility matrix are employed to find reducts. First relative discernibility relation of condition attribute is defined, indispensable and dispensable condition attributes are characterized by their relative discernibility relations and key sample pair set is defined for every condition attribute. With the key sample pair sets, all the sample pair selections can be found. Algorithms of computing one sample pair selection and finding reducts are also developed; comparisons with other methods of finding reducts are performed with several experiments which imply sample pair selection is effective as preprocessing step to find reducts.
Original languageEnglish
Article number6308684
Pages (from-to)2080-2093
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
Publication statusPublished - 5 Oct 2012


  • attribute reduction
  • Rough set
  • sample pair core
  • sample pair selection

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Sample pair selection for attribute reduction with rough set'. Together they form a unique fingerprint.

Cite this