Three-way decisions with intuitionistic fuzzy decision-theoretic rough sets based on point operators

被引:79
|
作者
Liang, Decui [1 ]
Xu, Zeshui [2 ]
Liu, Dun [3 ]
机构
[1] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610054, Peoples R China
[2] Sichuan Univ, Sch Business, Chengdu 610065, Sichuan, Peoples R China
[3] Southwest Jiaotong Univ, Sch Econ & Management, Chengdu 610031, Peoples R China
基金
美国国家科学基金会;
关键词
Three-way decisions; Decision-theoretic rough sets; Intuitionistic fuzzy sets; Point operator; ATTRIBUTE REDUCTION; MODEL; RISK; GRANULATION; WEB;
D O I
10.1016/j.ins.2016.09.039
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Three-way decisions with decision-theoretic rough sets (DTRSs) as a typical risk decision method, are generated by Bayesian decision theory and have three kinds of decision strategies, i.e., the acceptance decision, the deferment (non-commitment) decision and the rejection decision. The construction of three-way decisions under the complex decision-making context creates enormous challenges. The determination of loss function is one of key steps. In this paper, we discuss the decision principles of three-way decision rules based on the variation of loss functions with intuitionistic fuzzy sets (IFSs). More specifically, we introduce the intuitionistic fuzzy point operator (IFPO) into DTRSs and explore three-way decisions. Firstly, we construct a loss function matrix with the point operator and analyze its corresponding properties. IFPO implies one type of variation modes for the loss functions of three-way decisions. With respect to the point operator, we show that the prerequisites among loss functions still hold in each stage. Secondly, given the loss functions, we construct the corresponding three-way decision model and deduce three-way decisions. Finally, with the aid of information entropy theory, we further investigate which stage may be most suitable to make the decision. This study extends the range of applications of three-way decisions to the new intuitionistic fuzzy environment. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:183 / 201
页数:19
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