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Title: On the upper approximations of covering generalized rough sets
Authors: Tsang, ECC
Chen, D
Lee, JWT
Yeung, DS
Keywords: Approximation theory
Generalisation (artificial intelligence)
Rough set theory
Issue Date: 2004
Publisher: IEEE
Source: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004, v. 7, p. 4200-4203 How to cite?
Abstract: The covering generalized rough set is an improvement of Pawlak rough set to deal with more complex practical problems which the latter one cannot handle. However, many basic notions in this theory are not as widely agreeable as in the Pawlak rough set theory. We mainly improve the definition of upper approximation for covering generalized rough sets to make it more reasonable than the existing ones. Thus we set up a framework for the approximation operators of covering rough sets.
ISBN: 0-7803-8403-2
DOI: 10.1109/ICMLC.2004.1384576
Appears in Collections:Conference Paper

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