Attribute Reduction in Decision-Theoretic Rough Set Model: A Further Investigation

被引：0
|
作者：
Li, Huaxiong [1 ,2 ]
Zhou, Xianzhong [1 ]
Zhao, Jiabao [1 ]
Liu, Dun [3 ]
机构：
[1] Nanjing Univ, Sch Management & Engn, Nanjing 210093, Jiangsu, Peoples R China
[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Jiangsu, Peoples R China
[3] Southwest Jiaotong Univ, Sch Econ & Management, Chengdu 610031, Peoples R China
来源：
ROUGH SETS AND KNOWLEDGE TECHNOLOGY | 2011年 / 6954卷
基金：
中国国家自然科学基金;
关键词：
decision-theoretic rough set; attribute reduction; positive region; monotonicity; heuristic algorithm; MEMBERSHIP;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The monotonicity of positive region in PRS (Pawlak Rough Set) and DTRS (Decision-Theoretic Rough Set) are comparatively discussed in this paper. Theoretic analysis shows that the positive region in DTRS model may expand with the decrease of the attributes, which is essentially different from that of PRS model and leads to a new definition of attribute reduction in DTRS model. A heuristic algorithm for the newly defined attribute reduction in DTRS model is proposed, in which the positive region is allowed to expand instead of remaining unchanged in the process of deleting attributes. Results of experimental analysis are included to validate the theoretic analysis and quantify the effectiveness of the proposed attribute reduction algorithm.
引用
收藏
页码：466 / +
页数：3
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