Abstract
Rough set theory has been considered as a useful tool to model the vagueness, imprecision, and uncertainty, and has been applied successfully in many fields. In this paper, the basic concepts and properties of knowledge reduction based on evidence reasoning theory are discussed. Furthermore, the characterization and knowledge reduction approaches based on evidence reasoning theory are obtained.
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References
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17, 191–209 (1990)
Duntsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artificial Intelligence 106, 109–137 (1998)
Gediga, G., Duntsch, I.: Rough approximation quality revisited. Artificial Intelligence 132, 219–234 (2001)
Jensen, R., Shen, Q.: Fuzzy-rough sets assisted attribute selection. IEEE Transactions on Fuzzy Systems 15(1), 73–89 (2007)
Liang, J.Y., Dang, C.Y., Chin, K.S.: A new method for measuring uncertainty and fuzziness in rough set theory. International Journal of General Systems 31(4), 331–342 (2002)
Qian, Y.H., Liang, J.Y., Li, D.Y., Zhang, H.Y., Dang, C.Y.: Measures for evaluating the decision performance of a decision table in rough set theory. Infoormation Science 178, 181–202 (2002)
Xu, Z.B., Liang, J.Y., Dang, C.Y., Chin, K.S.: Inclusion degree: A perspective on measures for rough set data analysis. Informatin Sciences 141, 227–236 (2002)
Yao, Y.Y.: Information granulation and rough set approximation. International Journal of Intelligent Systems 16, 87–104 (2001)
Greco, S., Matarazzo, B., Słowiński, R.: A New Rough Set Approach to Multicriteria and Multiattribute Classification. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 60–67. Springer, Heidelberg (1998)
Greco, S., Matarazzo, B., Slowinski, R.: Rough set theory for multicriteria decision analysis. European Journal of Operational Research 129, 1–47 (2001)
Greco, S., Matarazzo, B., Slowinski, R.: Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European Journal of Operational Research 138, 247–259 (2001)
Qian, Y.H., Liang, J.Y., Dang, C.Y.: Interval ordered information systems. Comuters and Mathematics with Application 56(8), 1994–2009 (2008)
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Wang, H., Shi, H. (2012). Knowledge Reduction Based on Evidence Reasoning Theory in Interval Ordered Information Systems. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_4
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DOI: https://doi.org/10.1007/978-3-642-31576-3_4
Publisher Name: Springer, Berlin, Heidelberg
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