A New Uncertainty Measure of Rough Sets

摘要：

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.

作者: Shuhua Teng Dingqun Zhang Lingyun Cui Jixiang Sun Zhiyong Li

作者单位: College of Electronic Science & Engineering,National University of Defense Technology,Changsha,41007 Department of Electronics & Eelectrical Engineering,Nanyang Institute of Technology,Nanyang,473004,P Computer Science Department,Hebei Engineering and Technical College,Cangzhou,061001,P.R.China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

会议地点: 桂林

会议语种:英文

页码: 1189-1193

在线出版日期: 2009-12-19（万方平台首次上网日期，不代表论文的发表时间）