Triangular fuzzy decision-theoretic rough sets

被引:197
作者
Liang, Decui [1 ,2 ]
Liu, Dun [1 ]
Pedrycz, Witold [2 ,3 ]
Hu, Pei [1 ]
机构
[1] Southwest Jiaotong Univ, Sch Econ & Management, Chengdu 610031, Sichuan, Peoples R China
[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2G7, Canada
[3] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
基金
美国国家科学基金会; 中国博士后科学基金; 高等学校博士学科点专项科研基金;
关键词
Triangular fuzzy number; Linguistic variable; Loss function; Multiple attribute group decision making; Decision-theoretic rough sets; ATTRIBUTE REDUCTION; RISK ANALYSIS; REVISED METHOD; RANKING; MEMBERSHIP; NUMBERS; INFORMATION; MODEL; WEB;
D O I
10.1016/j.ijar.2013.03.014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Based on decision-theoretic rough sets (DTRS), we augment the existing model by introducing into the granular values. More specifically, we generalize a concept of the precise value of loss function to triangular fuzzy decision-theoretic rough sets (TFDTRS). Firstly, ranking the expected loss with triangular fuzzy number is analyzed. In light of Bayesian decision procedure, we calculate three thresholds and derive decision rules. The relationship between the values of the thresholds and the risk attitude index of decision maker presented in the ranking function is analyzed. With the aid of multiple attribute group decision making, we design an algorithm to determine the values of losses used in TFDTRS. It is achieved with the use of particle swarm optimization. Our study provides a solution in the aspect of determining the value of loss function of DTRS and extends its range of applications. Finally, an example is presented to elaborate on the performance of the TFDTRS model. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:1087 / 1106
页数:20
相关论文
共 67 条
[1]  
Abd El-Monsef M. M. E., 2007, INT J MATH MATH SCI, DOI [10.1155/2007/12714, DOI 10.1155/2007/12714.ARTICLE]
[2]  
[Anonymous], 1992, Fuzzy Multiple Attribute Decision Making: Methods and Applications
[3]   Revision of distance minimization method for ranking of fuzzy numbers [J].
Asady, B. .
APPLIED MATHEMATICAL MODELLING, 2011, 35 (03) :1306-1313
[4]   The revised method of ranking LR fuzzy number based on deviation degree [J].
Asady, B. .
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (07) :5056-5060
[5]  
Azam Nouman, 2012, Rough Sets and Knowledge Technology. Proceedings of the 7th International Conference, RSKT 2012, P399, DOI 10.1007/978-3-642-31900-6_49
[6]   A genetic design of linguistic terms for fuzzy rule based classifiers [J].
Cat Ho Nguyen ;
Pedrycz, Witold ;
Thang Long Duong ;
Thai Son Tran .
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2013, 54 (01) :1-21
[7]   Fuzzy risk analysis based on ranking generalized fuzzy numbers with different left heights and right heights [J].
Chen, Shyi-Ming ;
Munif, Abdul ;
Chen, Guey-Shya ;
Liu, Hsiang-Chuan ;
Kuo, Bor-Chen .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (07) :6320-6334
[8]   Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads [J].
Chen, Shyi-Ming ;
Chen, Jim-Ho .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) :6833-6842
[9]   Evaluating attack helicopters by AHP based on linguistic variable weight [J].
Cheng, CH ;
Yang, KL ;
Hwang, CL .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1999, 116 (02) :423-435
[10]   A revised method for ranking fuzzy numbers using maximizing set and minimizing set [J].
Chou, Shuo-Yan ;
Dat, Luu Quoc ;
Yu, Vincent F. .
COMPUTERS & INDUSTRIAL ENGINEERING, 2011, 61 (04) :1342-1348