Abstract
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.
This paper is partially supported by National Natural Science Foundation of P.R. China (No.60373111, No.60573068), Program for New Century Excellent Talents in University (NCET), Science and Technology Research Program of Chongqing Education Commission (No.040505, No.040509), Natural Science Foundation of Chongqing Science & Technology Commission (No.2005BA2003, No.2005BB2052).
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Zhao, J., Wang, G. (2006). Research on System Uncertainty Measures Based on Rough Set Theory. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_33
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DOI: https://doi.org/10.1007/11795131_33
Publisher Name: Springer, Berlin, Heidelberg
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