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Volume 36 Issue 12
Jan.  2015
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Liu Zhe-Xi, Yang Jian-Hong, Yang De-Bin, Li Min. Combination of Conflicting Evidence by Using the Total Uncertainty Degree of Information[J]. Journal of Electronics & Information Technology, 2014, 36(12): 2909-2914. doi: 10.3724/SP.J.1146.2014.00039
Citation: Liu Zhe-Xi, Yang Jian-Hong, Yang De-Bin, Li Min. Combination of Conflicting Evidence by Using the Total Uncertainty Degree of Information[J]. Journal of Electronics & Information Technology, 2014, 36(12): 2909-2914. doi: 10.3724/SP.J.1146.2014.00039

Combination of Conflicting Evidence by Using the Total Uncertainty Degree of Information

doi: 10.3724/SP.J.1146.2014.00039
  • Received Date: 2014-01-06
  • Rev Recd Date: 2014-04-11
  • Publish Date: 2014-12-19
  • The common way of conflicting evidence combination is to modify the basic probability mass assignment of evidence bodies by a certain indicator which can reflect or describe the information uncertainty of the conflicting evidence. In existing conflicting evidence combination methods, indicators such as the distance of evidence and ambiguity are used. However, these indicators reflect only one or several aspects of the characteristics of the conflicting information uncertainty. A novel method of conflicting evidence combination is proposed based on the total uncertainty degree of information. The concept of combined total uncertainty of information is defined based on Cartesian product. An approach of predicting the range of fused informations combined total uncertainty degree by the total uncertainty degree of each body of evidence before information fusion is also presented. Weights for each evidence body are obtained according to the total uncertainty degree of each evidence body and the combined total uncertainty on their Cartesian product. Then, the bodies of conflicting evidence are combined by the weighted average according to Dempsters rule. Results of numerical examples of information fusion show that, compared with the existing approaches, the total uncertainty degree of the combined information obtained by the proposed method is smaller, which means the combined information is more helpful to subsquent decision analysis and data applications.
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