Geodesic
Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems
Serdica Journal of Computing, Tome 7 (2013) no. 4, pp. 375-388.

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A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets.
Keywords: Rough Set, Tolerance-Based Rough Set, Decision System, Incomplete Decision System, Attribute Reduction, Reduct 
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     title = {Generalized {Discernibility} {Function} {Based} {Attribute} {Reduction} in {Incomplete} {Decision} {Systems}},
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Dinh, Vu Van; Giang, Nguyen Long. Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems. Serdica Journal of Computing, Tome 7 (2013) no. 4, pp. 375-388. http://geodesic.mathdoc.fr/item/SJC_2013_7_4_a4/