An Integrated Approach to Index Weight Based on Improved D-S Evidence Theory and Application of the Product Solution Evaluation

Abstract

To overcome the defect of the traditional method to determine attribute weight in the multiple attribute decision making problems, this paper proposed a kind of data fusion method based on The D-S Evidence Theory improved by The Grey Correlation Analysis. The method, firstly, calculated the basic probability assignment functions discount factor based on The Grey Correlation Analysis, then let the subjective weight and objective weight together based on D-S combination rules in order to avoid the deficiency of the single weighting method. Using the fusion of evaluation indexs subjective and objective weight of mobile phone product solution as an example, the results show that the proposed method is effective.

A novel evidence combination method based on stochastic approach for link-structure analysis algorithm and Lance-Williams distance

“…The comparative analysis shows that the proposed method can identify the target better. When the fourth piece of evidence is added m ( A ) = 0.8483, the results are more similar to the methods of Deng et al (2005) , Ye & Nie (2015) , Tang et al (2017) and Hu, Zhong & Liu (2016) when the fifth piece of evidence is added. When the fifth piece of evidence is added, the recognition rate of identifying target A is as high as 92.07%, which is higher than the recognition rate of the rest of the synthetic methods, and the accuracy of the method in this paper in identifying the target is higher when the evidence keeps increasing.…”

“…Moreover, the study in Ali, Dutta & Boruah (2012) proposes a new evidence combination method, but it is not apparent for target identification results. The methods in both Tang et al (2017) and Hu, Zhong & Liu (2016) use information entropy to deal with conflicting evidence and can identify the target, but the identification accuracy is not high when compared with the method proposed in this paper, and only one correction is made. Both Hu, Zhong & Liu (2016) and Tian, Ye & Wan (2021) use iterative methods to correct the evidence, which can accurately identify the target and have high recognition accuracy.…”

“…The methods in both Tang et al (2017) and Hu, Zhong & Liu (2016) use information entropy to deal with conflicting evidence and can identify the target, but the identification accuracy is not high when compared with the method proposed in this paper, and only one correction is made. Both Hu, Zhong & Liu (2016) and Tian, Ye & Wan (2021) use iterative methods to correct the evidence, which can accurately identify the target and have high recognition accuracy. However, the higher the accuracy requirement, the more iterations are needed, and the computation and workload are significantly increased; also, the computation is larger when dealing with larger-scale evidence.…”