Soft fuzzy rough sets and its application in decision making

被引:117
作者
Sun, Bingzhen [1 ,2 ]
Ma, Weimin [1 ]
机构
[1] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China
[2] Lanzhou Jiaotong Univ, Sch Traff & Transportat, Lanzhou 730070, Gansu, Peoples R China
基金
中国国家自然科学基金;
关键词
Soft sets; Pseudo fuzzy soft sets; Soft fuzzy rough sets; THEORETIC APPROACH; APPROXIMATION; REDUCTION; OPERATORS;
D O I
10.1007/s10462-011-9298-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, the theory and applications of soft set has brought the attention by many scholars in various areas. Especially, the researches of the theory for combining the soft set with the other mathematical theory have been developed by many authors. In this paper, we propose a new concept of soft fuzzy rough set by combining the fuzzy soft set with the traditional fuzzy rough set. The soft fuzzy rough lower and upper approximation operators of any fuzzy subset in the parameter set were defined by the concept of the pseudo fuzzy binary relation (or pseudo fuzzy soft set) established in this paper. Meanwhile, several deformations of the soft fuzzy rough lower and upper approximations are also presented. Furthermore, we also discuss some basic properties of the approximation operators in detail. Subsequently, we give an approach to decision making problem based on soft fuzzy rough set model by analyzing the limitations and advantages in the existing literatures. The decision steps and the algorithm of the decision method were also given. The proposed approach can obtain a object decision result with the data information owned by the decision problem only. Finally, the validity of the decision methods is tested by an applied example.
引用
收藏
页码:67 / 80
页数:14
相关论文
共 50 条
[11]   Intuitionistic fuzzy Hv-submodules [J].
Davvaz, B ;
Dudek, WA ;
Jun, YB .
INFORMATION SCIENCES, 2006, 176 (03) :285-300
[12]  
Deng JL, 1986, THEORY GREY SYSTEM
[13]   Time series forecasting through rule-based models obtained via rough sets [J].
Faustino, Claudio Paulo ;
Pinheiro, Carlos Alberto M. ;
Carpinteiro, Otavio A. ;
Lima, Isaias .
ARTIFICIAL INTELLIGENCE REVIEW, 2011, 36 (04) :299-310
[14]   Soft sets and soft rough sets [J].
Feng, Feng ;
Liu, Xiaoyan ;
Leoreanu-Fotea, Violeta ;
Jun, Young Bae .
INFORMATION SCIENCES, 2011, 181 (06) :1125-1137
[15]   Application of level soft sets in decision making based on interval-valued fuzzy soft sets [J].
Feng, Feng ;
Li, Yongming ;
Leoreanu-Fotea, Violeta .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2010, 60 (06) :1756-1767
[16]   An adjustable approach to fuzzy soft set based decision making [J].
Feng, Feng ;
Jun, Young Bae ;
Liu, Xiaoyan ;
Li, Lifeng .
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2010, 234 (01) :10-20
[17]   Soft semirings [J].
Feng, Feng ;
Jun, Young Bae ;
Zhao, Xianzhong .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2008, 56 (10) :2621-2628
[18]   VAGUE SETS [J].
GAU, WL ;
BUEHRER, DJ .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1993, 23 (02) :610-614
[19]   Rough set theory for the interval-valued fuzzy information systems [J].
Gong, Zengtai ;
Sun, Bingzhen ;
Chen, Degang .
INFORMATION SCIENCES, 2008, 178 (08) :1968-1985
[20]   A METHOD OF INFERENCE IN APPROXIMATE REASONING BASED ON INTERVAL-VALUED FUZZY-SETS [J].
GORZALCZANY, MB .
FUZZY SETS AND SYSTEMS, 1987, 21 (01) :1-17