DocumentCode
2542085
Title
Knowledge reduction in decision-theoretic rough set model based on connection degree
Author
Lv, Ping ; Qian, Jin ; Qian, Yuntao
Author_Institution
Sch. of Comput. Eng., Jiangsu Teachers Univ. of Technol., Changzhou, China
fYear
2012
fDate
29-31 May 2012
Firstpage
943
Lastpage
947
Abstract
Knowledge reduction is one of the most important research issues in decision-theoretic rough set model. This paper first defines a new attribute measure for a reduct preserving boundary region partition, then constructs a connection degree to evaluate the different candidate reducts, and finally proposes a knowledge reduction algorithm for decision-theoretic rough set model. Example analysis shows that this algorithm is valid.
Keywords
decision theory; knowledge acquisition; rough set theory; attribute measure; connection degree; decision-theoretic rough set model; knowledge reduction; reduct preserving boundary region partition; Complexity theory; Computational modeling; Mathematical model; Partitioning algorithms; Probabilistic logic; Rough sets; Knowledge reduction; connection degree; decision-theoretic rough set model;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location
Sichuan
Print_ISBN
978-1-4673-0025-4
Type
conf
DOI
10.1109/FSKD.2012.6233778
Filename
6233778
Link To Document