计算机科学技术学报(英文版)
計算機科學技術學報(英文版)
계산궤과학기술학보(영문판)
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
2009年
4期
786-797
,共12页
Dezert-Smarandache Theory (DSmT)%information fusion%qualitative reasoning%linguistic labels
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-Martfnez'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.