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Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems
Serdica Journal of Computing, Tome 7 (2013) no. 4, pp. 375-388 Cet article a éte moissonné depuis la source Bulgarian Digital Mathematics Library

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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 
@article{SJC_2013_7_4_a4,
     author = {Dinh, Vu Van and Giang, Nguyen Long},
     title = {Generalized {Discernibility} {Function} {Based} {Attribute} {Reduction} in {Incomplete} {Decision} {Systems}},
     journal = {Serdica Journal of Computing},
     pages = {375--388},
     year = {2013},
     volume = {7},
     number = {4},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/SJC_2013_7_4_a4/}
}
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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/