[1]LI Yage,YANG Hongzhi,XU Jiucheng.Triangular fuzzy number decision-theoretic rough sets under incomplete information systems[J].CAAI Transactions on Intelligent Systems,2016,11(4):449-458.[doi:10.11992/tis.201606016]
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Triangular fuzzy number decision-theoretic rough sets under incomplete information systems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 4 Page number: 449-458 Column: 学术论文—智能系统 Public date: 2016-07-25
Title:
Triangular fuzzy number decision-theoretic rough sets under incomplete information systems
Author(s):
LI Yage1; 4;  YANG Hongzhi2;  XU Jiucheng3
1. School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China;
2. Henan University of Economics and Law, Zhengzhou, Zhengzhou 450046, China;
3. College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China;
4. Department of Mathematics and Information Science, Xinxiang University, Xinxiang 453007, China
Keywords:
incomplete information system; interval value; triangular fuzzy number; decision-theoretic rough sets
CLC:
TP18
DOI:
10.11992/tis.201606016
Abstract:
Aiming at the problems that when using an interval value to represent an unknown parameter in an incomplete information system, the opportunity to obtain the value over the whole interval is considered to be equal, but the result may cause an over-large error. In order to solve this problem, a triangular fuzzy number was introduced into decision-theoretic rough sets, and a triangular fuzzy decision-theoretic rough set under incomplete information systems is proposed. Firstly, a new similarity relation was defined to describe incomplete information systems. Then, in view of the missing values, a model of triangular fuzzy number decision-theoretic rough sets was constructed to obtain the loss function. Finally, examples show that the proposed method not only makes up for deficiency in representation of the interval value, but also highlights the main value most likely to reduce the classification error.
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