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.
Keywords
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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM 38, 89–95 (1995)
Wang, G.Y.: Rough Set Theory & Knowledge Acquisition. Press of Xi’an Jiaotong University, Xi’an (2001)
Wang, G.Y., He, X.: Knowledge self-learning model based on rough set theory. Computer Science 9(special issue), 24–25 (2002)
Wang, G.Y., He, X.: A self-learning model under uncertain condition. Journal of Software 6, 1096–1102 (2003)
Düntsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artificial Intelligence 106, 109–137 (1998)
Chen, X.H., Zhu, S.J., Ji, Y.D.: Rule uncertainty measurements based on entropy and variable rough set. Journal of Tsinghua University (Science and Technology Edition) 3, 109–112 (2001)
Zhao, J., Wang, G.Y.: A data driven knowledge acquisition method based on system uncertainty. In: Proc. of the 4th Int. Conf. on Cognitive Informatics, Irvine, USA, pp. 267–275 (2005)
Ziarko, W.: Variable precision rough set model. Journal of Artificial Intelligence 46, 39–59 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11795131_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
eBook Packages: Computer ScienceComputer Science (R0)