Paper Title:
Methods of Assigning Attribute Weights Based on Rough Set and D-S Evidential Theory
  Abstract

Using the concept of support degree in rough set theory for reference, two new concepts, namely dominant support degree and recessive support degree were proposed in this paper. By utilizing these new concepts, two simple weight assignment methods by which dominant weight and recessive weight of attributes could be obtained. Nonetheless both the methods had drawbacks. Hence a further method was developed below. The first step was to construct two items of evidence by deploying dominant weights vector and recessive weights vector of the attribute set and then assigned weights for the evidence to construct weighted evidence. Next was to combine the two items of weighted evidence in reference to the D-S evidential theory. Finally the weight assignments for the attributes could be procured after further processing the combination result. Furthermore, the concept of joint dominant weight of multiple attributes and related methods to assign weights for attributes in some particular situations were proposed. The rationality and wide scope of application of above methods were proven.

  Info
Periodical
Key Engineering Materials (Volumes 419-420)
Edited by
Daizhong Su, Qingbin Zhang and Shifan Zhu
Pages
249-252
DOI
10.4028/www.scientific.net/KEM.419-420.249
Citation
L. L. Li, M. H. Wang, F. Zheng, Y. G. Duanmu, "Methods of Assigning Attribute Weights Based on Rough Set and D-S Evidential Theory", Key Engineering Materials, Vols. 419-420, pp. 249-252, 2010
Online since
October 2009
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Wei Wang, Wei Du
Abstract:Attribute reduction can simplify condition attributes in the decision table, while the simplified decision table has same functionality with...
266
Authors: Chun Fei Yuan, Jing Cai, Yi Ming Xu
Chapter 11: Computer Application and CAD/CAE
Abstract:Modern fault diagnosis system always is a dynamic, flexible and uncertain complicated system, so many fault diagnosis methods are not...
3644
Authors: Na Su, Feng Feng Liao, Zhe Hui Wu
Chapter 6: Algorithm Design
Abstract:The independency between two attribute subsets can be verified based on Chi square statistic to reduce candidate sets. Based on this measure,...
1543
Authors: Ye Li, Bing Lu
Chapter 7: Information Engineering and Technology
Abstract:Aiming at improving the disvantage of single attribute analysis in rough set, the problem of combined attributes analysis is researched in...
1404
Authors: Xi Zhou, Ke Luo
Chapter 7: Image Processing, Data Mining and Information Engineering
Abstract:Naïve Bayes classifier was generally considered as a simple and efficient classification method. However, its classification performance was...
2108