Uncertainty Measurement for a Fuzzy Relation Information System

被引：83
作者：
Li, Zhaowen [1 ]
Zhang, Pengfei [2 ]
Ge, Xun [3 ]
Xie, Ningxin [4 ]
Zhang, Gangqiang [4 ]
Wen, Ching-Feng [5 ,6 ,7 ]
机构：
[1] Yulin Normal Univ, Dept Guangxi Educ, Key Lab Complex Syst Optimizat & Big Data Proc, Yulin 537000, Peoples R China
[2] Guangxi Univ Nationalities, Sch Sci, Nanning 530006, Peoples R China
[3] Soochow Univ, Sch Math Sci, Suzhou 215006, Peoples R China
[4] Guangxi Univ Nationalities, Sch Software & Informat Secur, Nanning 530006, Peoples R China
[5] Kaohsiung Med Univ, Ctr Fundamental Sci, Kaohsiung 80708, Taiwan
[6] Kaohsiung Med Univ, Res Ctr Nonlinear Anal & Optimizat, Kaohsiung 80708, Taiwan
[7] Kaohsiung Med Univ Hosp, Dept Med Res, Kaohsiung 80708, Taiwan
来源：
IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2019年 / 27卷 / 12期
关键词：
Information systems; Uncertainty; Measurement uncertainty; Rough sets; Knowledge based systems; Entropy; Fuzzy sets; Characterization; entropy; effectiveness; fuzzy relation; fuzzy relation information system; granularity; information structure; measurement; uncertainty; DIMENSIONALITY REDUCTION; GRANULARITY MEASURES; ENTROPY MEASURES; ROUGH SETS; APPROXIMATION; GRANULATION; INTERVAL; RULES;
D O I：
10.1109/TFUZZ.2019.2898158
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A fuzzy relation information system may be viewed as an information system with fuzzy relations. Uncertainty measurement is a critical evaluating tool. This paper investigates uncertainty measurement for a fuzzy relation information system. The concept of information structures in a fuzzy relation information system is first described by using set vectors. Then, dependence between information structures in a fuzzy relation information system is given. Next, the axiom definition of the granularity measurement of the uncertainty for fuzzy relation information systems is proposed by means of its information structures. Based upon this axiom definition, information granulation and rough entropy in a fuzzy relation information system are proposed. Moreover, information entropy, information amount, joint entropy, and condition entropy in a fuzzy relation information system are also considered. To show the feasibility of the proposed measures for uncertainty of a fuzzy relation information system, effectiveness analysis is conducted from the angle of statistics. Finally, characterizations of fuzzy relation information systems under a compatible homomorphism are obtained. These results will be helpful for understanding the essence of uncertainty in a fuzzy relation information system.
引用
收藏
页码：2338 / 2352
页数：15
相关论文
共 49 条
[1]  
Ali MI, 2011, HACET J MATH STAT, V40, P383
[2]   Information-theoretic measures of uncertainty for rough sets and rough relational databases [J].
Beaubouef, T ;
Petry, FE ;
Arora, G .
INFORMATION SCIENCES, 1998, 109 (1-4) :185-195
[3]   Sequential covering rule induction algorithm for variable consistency rough set approaches [J].
Blaszczynski, Jerzy ;
Slowinski, Roman ;
Szelag, Marcin .
INFORMATION SCIENCES, 2011, 181 (05) :987-1002
[4]   Fusion of local normalization and Gabor entropy weighted features for face identification [J].
Cament, Leonardo A. ;
Castillo, Luis E. ;
Perez, Juan P. ;
Galdames, Francisco J. ;
Perez, Claudio A. .
PATTERN RECOGNITION, 2014, 47 (02) :568-577
[5]   Attribute selection with fuzzy decision reducts [J].
Cornelis, Chris ;
Jensen, Richard ;
Hurtado, German ;
Slezak, Dominik .
INFORMATION SCIENCES, 2010, 180 (02) :209-224
[6]   Entropy measures and granularity measures for set-valued information systems [J].
Dai, Jianhua ;
Tian, Haowei .
INFORMATION SCIENCES, 2013, 240 :72-82
[7]   Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A case study of a mining project in Peru [J].
Delgado, Alexi ;
Romero, I. .
ENVIRONMENTAL MODELLING & SOFTWARE, 2016, 77 :108-121
[8]   ROUGH FUZZY-SETS AND FUZZY ROUGH SETS [J].
DUBOIS, D ;
PRADE, H .
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 1990, 17 (2-3) :191-209
[9]   Another View on Generalized Intuitionistic Fuzzy Soft Sets and Related Multiattribute Decision Making Methods [J].
Feng, Feng ;
Fujita, Hamido ;
Ali, Muhammad Irfan ;
Yager, Ronald R. ;
Liu, Xiaoyan .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2019, 27 (03) :474-488
[10]   Fuzzy rough sets and multiple-premise gradual decision rules [J].
Greco, S ;
Inuiguchi, M ;
Slowinski, R .
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2006, 41 (02) :179-211
12345