The Dezert-Smarandache theory （DSmT） is a useful method for dealing with uncertainty problems. It is more efficient in combining conflicting evidence. Therefore, it has been successfully applied in data fusion and o...The Dezert-Smarandache theory （DSmT） is a useful method for dealing with uncertainty problems. It is more efficient in combining conflicting evidence. Therefore, it has been successfully applied in data fusion and object recognition. However, there exist shortcomings in its combination rule. An efficient combination rule is presented, that is, the evidence＇s conflicting probability is distributed to every proposition based on remaining the focal elements of conflict. Experiments show that the new combination rule improves the reliability and rationality of the combination results. Although evidences conflict another one highly, good combination results are also obtained.展开更多
Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this...Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models （i.e., a distribution function model （DFM） and an improved Herrera-Martinez＇s model） jointly with the fusion rules developed in Dezert-Smarandache Theory （DSmT）, in order to combine efficiently qualitative information expressed in term of qualitative belief functions. The two models both preserve the precision and improve the efficiency of the fusion of linguistic information expressing the global expert＇s opinion. However, DFM is more general and efficient than the latter, especially for unbalanced linguistic labels. Some simple examples are also provided to show how the 2-Tuple qualitative fusion rules are performed and their advantages.展开更多
基金supported by the National Natural Science Foundation of China (60572161)Excellent Ph.D Paper Author Foundation of China (200443)
文摘The Dezert-Smarandache theory （DSmT） is a useful method for dealing with uncertainty problems. It is more efficient in combining conflicting evidence. Therefore, it has been successfully applied in data fusion and object recognition. However, there exist shortcomings in its combination rule. An efficient combination rule is presented, that is, the evidence＇s conflicting probability is distributed to every proposition based on remaining the focal elements of conflict. Experiments show that the new combination rule improves the reliability and rationality of the combination results. Although evidences conflict another one highly, good combination results are also obtained.
基金supported by the National Natural Science Foundation of China under Grant No.60804063supported by the National Natural Science Foundation of China under GrantNo.60804063one subproject of Jiangsu Province Science and Technology Transformation Project under Grant No.B3A2007058
文摘Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models （i.e., a distribution function model （DFM） and an improved Herrera-Martinez＇s model） jointly with the fusion rules developed in Dezert-Smarandache Theory （DSmT）, in order to combine efficiently qualitative information expressed in term of qualitative belief functions. The two models both preserve the precision and improve the efficiency of the fusion of linguistic information expressing the global expert＇s opinion. However, DFM is more general and efficient than the latter, especially for unbalanced linguistic labels. Some simple examples are also provided to show how the 2-Tuple qualitative fusion rules are performed and their advantages.
