Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6856492 | Information Sciences | 2018 | 26 Pages |
Abstract
Dempster-Shafer theory manages uncertain information and offers a useful fusion tool for decision-making. However, the quality of combining is affected by conflicting information, especially when the sources of evidence are unreliable. A number of methods for discounting unreliable sources of evidence have been proposed. In these approaches, the estimation of the discounting factors is crucial, mainly when prior knowledge is unavailable. Most discounting methods assume that the distance between bodies of evidence is the only factor for conflict estimation. However, considering just one factor is not reliable enough to assess the reliability degrees, since the confusion, disparity, and imperfection of information provided by the sources of evidence are due to different types of conflict. In this paper, we proposed a multi-criteria method to estimate the reliability factors. We defined a method to select a comprehensive set of criteria, which each criterion takes into account the uncertainty of each source of evidence. Then, we proposed an aggregation method to estimate the reliability degree. Finally, we extended our proposed reliability degrees in a sequential discounting approach. We verified our suggested discounting method through two designed experiments. The results showed that our approach was efficient in combining the unreliable sources of evidence. Moreover, our proposed reliability degree was more robust in outliers than the previous methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Atiye Sarabi-Jamab, Babak N. Araabi,