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.

• Periodical: Key Engineering Materials (Volumes 419-420)
• Pages: 249-252
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.419-420.249
• Online since: October 2009
• Authors: Ling Ling Li, Ming Hui Wang, Fei Zheng, Yong Guang Duanmu
• Keywords: Evidential Theory, Property Weight, Rough Set (RS), Weight Assignment

References

[1] Li Lingling, Li Zhigang and Zhou Chunlai, in: Proc. of Fifth World Congress on Intelligent Control and Automation (2004), p.4092.

[2] Jin Juliang, Cheng Jilin and Wei Yiming: Journal of Hydroelectric Engineering, Vol. 26(1) (2007), p.28.

[3] Wang Jin-ye, Bi Xun-yin and Li Ying-jian: Journal of Shandong University of Science and Technology, Vol. 25(3) (2006), p.85.

[4] Liu Zhi-ming, Wu Ming-fen and Xu Yong: Journal of Wuyi University (Natural Science Edition), Vol. 20(3) (2006), p.45.

[5] Sun Bin: Journal of Liaoning Technical University, Vol. 27(5) (2008), p.661.

[6] Zhong Jia-ming and Li Ding-fang: Computer Engineering and Applications, Vol. 44(2) (2008), p.51.

[7] Liu Bing-xiang and Li Hai-lin: Control and Decision, Vol. 22(12) (2007), p.1437.

[8] Zhu Chun-jiang and Tang De-shan: Soft Science, Vol. 20(4) (2006), p.16.

[9] Chen Wei and Xia Jian-hua: Mathematics in Practice and Theory, Vol. 37(1) (2007), p.17.

[10] Zhang Wenxiu, Wu Weizhi and Liang Jiye: rough set Theory and Methods, Second Edition (Science Publications, Beijing, China 2005).

[11] Cai Zixing and Xu Guangyou: Artificial Intelligence and its Applications, Third Edition (Tsinghua Publications, Beijing, China 2004).