A new uncertainty measure of rough sets

  • Authors:

  • Shuhua Teng;Dingqun Zhang;Lingyun Cui;Jixiang Suna;Zhiyong Lia

  • Affiliations:

  • College of Electronic Science & Engineering, National University of Defense Technology, Changsha, P.R.China;Department of Electronics & Eelectrical Engineering, Nanyang Institute of Technology, Nanyang, P.R.China;Computer Science Department, Hebei Engineering and Technical College, Cangzhou, P.R.China;College of Electronic Science & Engineering, National University of Defense Technology, Changsha, P.R.China;College of Electronic Science & Engineering, National University of Defense Technology, Changsha, P.R.China

  • Venue:
  • ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Uncertainty measure is a key issue for knowledge discovery and data mining. Rough set theory (RST) is an important tool for measuring and handling uncertain information. Although many RST-based methods to measure system uncertainty have been investigated, the existing measures are not able to characterize well the imprecision of a rough set. To overcome the shortcomings, we present a well-justified measure of uncertainty based on discernibility capability of attributes. The theoretical analysis is backed up with numerical examples to prove that our new method does not only overcome the limitations of the existing measures but also consist with human cognition.