A vague-rough set approach for uncertain knowledge acquisition

  • Authors:

  • Lin Feng;Tianrui Li;Da Ruan;Shirong Gou

  • Affiliations:

  • College of Computer Science, Sichuan Normal University, Chengdu 610101, PR China and School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, PR China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, PR China;Belgian Nuclear Research Centre (SCKEN), Boeretang 200, 2400 Mol, Belgium and Department of Applied Mathematics & Computer Science, Ghent University, Krijgslaan 281 (S9), 9000 Gent, Belgium;College of Computer Science, Sichuan Normal University, Chengdu 610101, PR China

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2011

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Abstract

By combining both vague sets and rough sets in fuzzy data processing, we propose a vague-rough set approach for extracting knowledge under uncertain environments. We compute all attribute reductions using the vague-rough lower approximation distribution, concepts of attribute reduction and the discernibility matrix in a vague decision information system (VDIS). Research results for extracting decision rules from the VDIS show the proposed approaches extend the corresponding method in classical rough set theory and provide a new avenue to uncertain vague knowledge acquisition.