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Qian Y.,Key Laboratory of Computational Intelligence and Chinese | Qian Y.,Shanxi University | Liang J.,Key Laboratory of Computational Intelligence and Chinese | Liang J.,Shanxi University | And 6 more authors.
Knowledge-Based Systems

This article deals with approaches to attribute reductions in inconsistent incomplete decision table. The main objective of this study is to extend a kind of attribute reductions called a lower approximation reduct and an upper approximation reduct, which preserve the lower/upper approximation distribution of a target decision. Several judgement theorems of a lower/upper approximation consistent set in inconsistent incomplete decision table are educed. Then, the discernibility matrices associated with the two approximation reductions are examined as well, from which we can obtain approaches to attribute reduction of an incomplete decision table in rough set theory. © 2010 Elsevier B.V. All rights reserved. Source

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