A relational perspective of attribute reduction in rough set-based data analysis

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

*Corresponding author for this work

研究成果: Article同行評審

12 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 principle. That is, the relationship between the class labels of a pair of objects depends on component-wise comparison of their condition attributes. The larger the number of condition attributes compared, the greater the probability that the dependency will hold. Thus, elimination of condition attributes may cause more object pairs to violate the dependency principle. 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 dependency principle. 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
頁(從 - 到)270-278
頁數9
期刊European Journal of Operational Research
213
發行號1
DOIs
出版狀態Published - 16 八月 2011

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