Linguistic Distribution Assessments with Interval Symbolic Proportions

In linguistic distribution assessments, symbolic proportions are assigned to all the linguistic terms. As a natural generation, we propose the concept of distribution assessments with interval symbolic proportion in a linguistic term set, and then study the operational laws of linguistic distribution assessments with interval symbolic proportion. Then, the weighted averaging operator and the ordered weighted averaging operator for linguistic distribution assessments with interval symbolic proportion are presented. Finally, two examples are presented for demonstrating the applicability of the proposed approach for computing with words.

[1]  Jin-Hsien Wang,et al.  An Approach to Computing With Words Based on Canonical Characteristic Values of Linguistic Labels , 2007, IEEE Transactions on Fuzzy Systems.

[2]  Francisco Herrera,et al.  Computing with words in decision making: foundations, trends and prospects , 2009, Fuzzy Optim. Decis. Mak..

[3]  Francisco Herrera,et al.  A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Shui Yu,et al.  Linguistic Computational Model Based on 2-Tuples and Intervals , 2013, IEEE Transactions on Fuzzy Systems.

[5]  Jonathan Lawry,et al.  An Alternative Approach to Computing with Words , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[6]  Enrique Herrera-Viedma,et al.  Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information , 2010, Knowl. Based Syst..

[7]  Jian-Bo Yang,et al.  The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees , 2006, Eur. J. Oper. Res..

[8]  Francisco Herrera,et al.  Hesitant Fuzzy Linguistic Term Sets for Decision Making , 2012, IEEE Transactions on Fuzzy Systems.

[9]  H. Ishibuchi,et al.  Multiobjective programming in optimization of the interval objective function , 1990 .

[10]  Piero P. Bonissone,et al.  Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity , 1985, UAI.

[11]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[12]  Francisco Herrera,et al.  Computing with Words in Decision support Systems: An overview on Models and Applications , 2010, Int. J. Comput. Intell. Syst..

[13]  Francisco Herrera,et al.  A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..

[14]  Francisco Herrera,et al.  A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets , 2013, Inf. Sci..

[15]  Tapan Kumar Pal,et al.  On comparing interval numbers , 2000, Eur. J. Oper. Res..

[16]  José L. Verdegay,et al.  On aggregation operations of linguistic labels , 1993, Int. J. Intell. Syst..

[17]  Francisco Herrera,et al.  An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges , 2012, Inf. Sci..

[18]  Yin-Feng Xu,et al.  Consistency and consensus measures for linguistic preference relations based on distribution assessments , 2014, Inf. Fusion.

[19]  Zeshui Xu,et al.  Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment , 2004, Inf. Sci..

[20]  Jin-Hsien Wang,et al.  A new version of 2-tuple fuzzy linguistic representation model for computing with words , 2006, IEEE Trans. Fuzzy Syst..