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Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT

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Abstract

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-Martínez’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.

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Correspondence to Xin-De Li.

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Xin-De Li (Presently, he is taking charge of one project supported by the National Natural Science Foundation of China under Grant No.60804063 and one subproject of Jiangsu Province Science and Technology Transformation Project under Grant No. BA2007058.)

This work is supported by the National Natural Science Foundation of China under Grant No. 60804063.

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Li, XD., Smarandache, F., Dezert, J. et al. Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT. J. Comput. Sci. Technol. 24, 786–797 (2009). https://doi.org/10.1007/s11390-009-9258-8

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  • DOI: https://doi.org/10.1007/s11390-009-9258-8

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