Approaches to knowledge reduction based on variable precision rough set model

  • Authors:

  • Ju-Sheng Mi;Wei-Zhi Wu;Wen-Xiu Zhang

  • Affiliations:

  • College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, Hebei 050016, PR China and Institute for Information and System Sciences, Faculty of Science, Xi'an Jiaotong ...;Institute for Information and System Sciences, Faculty of Science, Xi'an Jiaotong University, Xi'an, Shaan'xi 710049, PR China and Information College, Zhejiang Ocean University, Zhoushan, Zhejian ...;Institute for Information and System Sciences, Faculty of Science, Xi'an Jiaotong University, Xi'an, Shaan'xi 710049, PR China

  • Venue:
  • Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
  • Year:
  • 2004

Quantified Score

Hi-index 0.01

Visualization

Abstract

This paper deals with approaches to knowledge reduction based on variable precision rough set model. The concepts of β lower distribution reduct and β upper distribution reduct based on variable precision rough sets (VPRS) are first introduced. Their equivalent definitions are then given, and the relationships among β lower and β upper distribution reducts and alternative types of knowledge reduction in inconsistent systems are investigated. It is proved that for some special thresholds, β lower distribution reduct is equivalent to the maximum distribution reduct, whereas β upper distribution reduct is equivalent to the possible reduct. The judgement theorems and discernibility matrices associated with the β lower and β upper distribution reducts are also established, from which we can obtain the approaches to knowledge reduction in VPRS.