Research on System Uncertainty Measures Based on Rough Set Theory

Due to various inherent uncertain factors, system uncertainty is an important intrinsic feature of decision information systems. It is important for data mining tasks to reasonably measure system uncertainty. Rough set theory is one of the most successful tools for measuring and handling uncertain information. Various methods based on rough set theory for measuring system uncertainty have been investigated. Their algebraic characteristics and quantitative relations are analyzed and disclosed in this paper. The results are helpful for selecting proper uncertainty measures or even developing new uncertainty measures for specific applications

[1]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[2]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[3]  Guo-Yin Wang,et al.  A data-driven knowledge acquisition method based on system uncertainty , 2005, Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005)..

[4]  Jerzy W. Grzymala-Busse,et al.  Rough sets : New horizons in commercial and industrial AI , 1995 .

[5]  Ivo Düntsch,et al.  Uncertainty Measures of Rough Set Prediction , 1998, Artif. Intell..

[6]  Wang Guo-yin,et al.  A Self-Learning Model under Uncertain Condition , 2003 .