標題: A relational perspective of attribute reduction in rough set-based data analysis
作者: Fan, Tuan-Fang
Liau, Churn-Jung
Liu, Duen-Ren
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Rough sets;Decision analysis;Fuzzy sets;Attribute reduction;Relational information system
公開日期: 16-八月-2011
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. (C) 2010 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.ejor.2010.08.017
http://hdl.handle.net/11536/20190
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2010.08.017
期刊: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume: 213
Issue: 1
起始頁: 270
結束頁: 278
顯示於類別:期刊論文


文件中的檔案:

  1. 000291082100025.pdf