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Fusion of imprecise qualitative information

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Abstract

In this paper, we present a new 2-tuple linguistic representation model, i.e. Distribution Function Model (DFM), for combining imprecise qualitative information using fusion rules drawn from Dezert-Smarandache Theory (DSmT) framework. Such new approach allows to preserve the precision and efficiency of the combination of linguistic information in the case of either equidistant or unbalanced label model. Some basic operators on imprecise 2-tuple labels are presented together with their extensions for imprecise 2-tuple labels. We also give simple examples to show how precise and imprecise qualitative information can be combined for reasoning under uncertainty. It is concluded that DSmT can deal efficiently with both precise and imprecise quantitative and qualitative beliefs, which extends the scope of this theory.

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Authors and Affiliations

  1. Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing, 210096, China

    Xinde Li & Xianzhong Dai

  2. ONERA (The French Aerospace Lab.), 29 Avenue de la Division Leclerc, 92320, Châtillon, France

    Jean Dezert

  3. Chair of Math. & Sciences Dept., University of New Mexico, 200 College Road, Gallup, NM, 87301, USA

    Florentin Smarandache

Authors
  1. Xinde Li
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  2. Xianzhong Dai
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  3. Jean Dezert
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  4. Florentin Smarandache
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Correspondence to Xinde Li.

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Li, X., Dai, X., Dezert, J. et al. Fusion of imprecise qualitative information. Appl Intell 33, 340–351 (2010). https://doi.org/10.1007/s10489-009-0170-2

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  • DOI: https://doi.org/10.1007/s10489-009-0170-2

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