Constraint-based attribute reduction in rough set analysis

Tuan Fang Fan*, Churn Jung Liau, Duen-Ren Liu

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Attribute reduction is very important in rough set-based data analysis (RSDA) because it can be used to simplify the induced decision rules without reducing the classification accuracy. The notion of reduct plays a key role in rough set-based attribute reduction. In rough set theory, a reduct is generally defined as a minimal subset of attributes that can classify the same domain of objects as unambiguously as the original set of attributes. Nevertheless, from a relational perspective, RSDA relies on a kind of dependency constraint. That is, the relationship between the class labels of a pair of objects depends on the componentwise comparison of their condition attributes. The larger the number of condition attributes compared, the greater the probability that the constraint will hold. Thus, elimination of condition attributes may cause more object pairs to violate the constraint. Based on this observation, a reduct can be defined alternatively as a minimal subset of attributes that does not increase the number of objects violating the constraint. While the alternative definition coincides with the original one in ordinary RSDA, it is more easily generalized to cases of fuzzy RSDA and relational data analysis.

原文English
主出版物標題2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
頁面3971-3976
頁數6
DOIs
出版狀態Published - 1 12月 2010
事件2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 - Istanbul, Turkey
持續時間: 10 10月 201013 10月 2010

出版系列

名字Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN(列印)1062-922X

Conference

Conference2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
國家/地區Turkey
城市Istanbul
期間10/10/1013/10/10

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