Positive approximation and converse approximation in interval-valued fuzzy rough sets

被引:32
|
作者
Cheng, Yi [1 ,2 ]
Miao, Duoqian [1 ]
Feng, Qinrong [1 ]
机构
[1] Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China
[2] Sichuan Coll Architectural Technol, Dept Equipment & Engn, Deyang 618000, Peoples R China
基金
中国国家自然科学基金;
关键词
Interval-valued fuzzy rough sets; Positive approximation; Converse approximation; Rule extraction; Granulation order; DECISION PERFORMANCE; ATTRIBUTE REDUCTION; INDUCTION;
D O I
10.1016/j.ins.2011.01.033
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Methods of fuzzy rule extraction based on rough set theory are rarely reported in incomplete interval-valued fuzzy information systems. Thus, this paper deals with such systems. Instead of obtaining rules by attribute reduction, which may have a negative effect on inducting good rules, the objective of this paper is to extract rules without computing attribute reducts. The data completeness of missing attribute values is first presented. Positive and converse approximations in interval-valued fuzzy rough sets are then defined, and their important properties are discussed. Two algorithms based on positive and converse approximations, namely, mine rules based on the positive approximation (MRBPA) and mine rules based on the converse approximation (MRBCA), are proposed for rule extraction. The two algorithms are evaluated by several data sets from the UC Irvine Machine Learning Repository. The experimental results show that MRBPA and MRBCA achieve better classification performances than the method based on attribute reduction. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:2086 / 2110
页数:25
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