Result 1 to 20 of 61 total
Bayesian rough set model: a further investigation. (English)
Int. J. Approx. Reasoning 53, No. 4, 541-557 (2012).
1
On attribute reduction with intuitionistic fuzzy rough sets. (English)
Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 20, No. 1, 59-76 (2012).
2
Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets. (English)
Inf. Sci. 181, No. 23, 5169-5179 (2011).
3
An incremental algorithm for Pawlak reduction based on an attribute order. (Chinese)
J. Southwest Jiaotong Univ. 46, No. 3, 461-468 (2011).
4
An algorithm for numerical attribute reduction based on discernibility matrix. (Chinese)
J. Jiangxi Norm. Univ., Nat. Sci. Ed. 35, No. 2, 135-139 (2011).
5
An effective increment algorithm for attribute reduction. (Chinese)
Control Decis. 26, No. 4, 495-500 (2011).
6
Approximate assignment reduction in set-valued fuzzy decision information systems. (Chinese)
J. Sichuan Norm. Univ., Nat. Sci. 34, No. 1, 23-27 (2011).
7
Discernibility-matrix method based on the hybrid of equivalence and dominance relations. (English)
Kuznetsov, Sergei O. (ed.) et al., Rough sets, fuzzy sets, data mining and granular computing. 13th international conference, RSFDGrC 2011, Moscow, Russia, June 25‒27, 2011. Proceedings. Berlin: Springer (ISBN 978-3-642-21880-4/pbk). Lecture Notes in Computer Science 6743. Lecture Notes in Artificial Intelligence, 231-239 (2011).
8
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation. (English)
Int. J. Approx. Reasoning 52, No. 2, 212-230 (2011).
9
Reducts in object oriented information system. (English)
Int. J. Contemp. Math. Sci. 5, No. 33-36, 1771-1787 (2010).
10
A quick incremental updating algorithm for computing core attributes. (English)
Yu, Jian (ed.) et al., Rough set and knowledge technology. 5th international conference, RSKT 2010, Beijing, China, October 15‒17, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-16247-3/pbk). Lecture Notes in Computer Science 6401. Lecture Notes in Artificial Intelligence, 249-256 (2010).
11
A unified approach to reducts in dominance-based rough set approach. (English)
Soft Comput. 14, No. 5, 507-515 (2010).
12
An efficient algorithm for Pawlak reduction based on simplified discernibility matrix. (English)
Cao, Bing-yuan (ed.) et al., Fuzzy information and engineering. Vol. 1. Proceedings of the third annual conference on fuzzy information and engineering (ACFIE 2008), Haikou, China, December 5‒10, 2008. Berlin: Springer (ISBN 978-3-540-88913-7/pbk; 978-3-540-88914-4/ebook). Advances in Soft Computing 54, 610-619 (2009).
13
The attribute reduction of probabilistic information systems. (Chinese)
J. Hebei Norm. Univ., Nat. Sci. Ed. 33, No. 6, 724-729 (2009).
14
Attribute dependency functions considering data efficiency. (English)
Int. J. Approx. Reasoning 51, No. 1, 89-98 (2009).
15
Efficient algorithm of attribute reduction based on Skowron’s discernibility matrix. (Chinese)
J. Univ. Sci. Technol. Beijing (Chin. Ed.) 31, No. 3, 398-404 (2009).
16
A decision reduction algorithm based on the discernibility matrix in the fuzzy object system. (Chinese)
Math. Pract. Theory 39, No. 5, 103-107 (2009).
17
Attribute reduction in formal contexts based on a new discernibility matrix. (English)
J. Appl. Math. Comput. 30, No. 1-2, 305-314 (2009).
18
Discernibility matrix simplification for constructing attribute reducts. (English)
Inf. Sci. 179, No. 7, 867-882 (2009).
19
A novel condensing tree structure for rough set feature selection. (English)
Neurocomputing 71, No. 4-6, 1092-1100 (2008).
20
Result 1 to 20 of 61 total
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