Decision Making in Incomplete Information System Based on Decision-Theoretic Rough Sets

Author(s):  
Xiaoping Yang ◽  
Haiguang Song ◽  
Tong-Jun Li
2021 ◽  
pp. 1-13
Author(s):  
Jing Pang ◽  
Bingxue Yao ◽  
Lingqiang Li

In this paper, we point out that Lin’s general neighborhood systems-based rough set model is an extension of Qian’s optimistic rough set model, and thus called optimistic general neighborhood systmes-based rough set model. Then we present a new rough set model based on general neighborhood systems, and prove that it is an extension of Qian’s pessimistic rough set model. Later, we study the basic properties of the proposed pessimistic rough sets, and define the serial, reflexive, symmetric, transitive and Euclidean conditions for general neighborhood systems, and explore the further properties of related rough sets. Furthermore, we apply the pessimistic general neighborhood systems-based rough set model in the research of incomplete information system, and build a three-way decision model based on it. A simple practical example to show the effectiveness of our model is also presented.


2020 ◽  
Vol 39 (1) ◽  
pp. 81-105
Author(s):  
Muhammad Uzair Khan ◽  
Abbas Ali ◽  
Noor Rehman ◽  
Saleem Abdullah

Author(s):  
XIBEI YANG ◽  
ZEHUA CHEN ◽  
HUILI DOU ◽  
MING ZHANG ◽  
JINGYU YANG

The neighborhood system based rough set is a generalization of Pawlak's rough set model since the former uses the neighborhood system instead of the partition for constructing target approximation. In this paper, the neighborhood system based rough set approach is employed to deal with the incomplete information system. By the coverings induced by the maximal consistent blocks and the support sets of the descriptors, respectively, two neighborhood systems based rough sets are explored. By comparing with the original maximal consistent block and descriptor based rough sets, the neighborhood system based rough sets hold the same lower approximations and the smaller upper approximations. Furthermore, the concept of attribute reduction is introduced into the neighborhood systems and the corresponding rough sets. The judgement theorems and discernibility functions to compute reducts are also presented. Some numerical examples are employed to substantiate the conceptual arguments.


Sign in / Sign up

Export Citation Format

Share Document