A Decision Making Approach Based on Hesitant Fuzzy Linguistic-Valued Credibility Reasoning

Credibility reasoning has attracted a lot of attention due to its distinguished power and efficiency in representing uncertainty vagueness within the process decision making. Aiming at problem inaccurate credibility estimation making experts express hesitant preferences better evaluation process, this paper introduces fuzzy linguistic term set into reasoning. First, we propose linguistic-valued (HLCF), establish knowledge representation model credibility. Then, order solve incomplete information an complement algorithm based on maximum similarity is constructed. After that, algorithms single rule multiple rules parallel relationship are proposed enrich base get more accurate results. The closeness degrees between conclusions each alternative after expected value calculated, so as select most suitable alternative. Finally, practical example which concerned social risk analysis given illustrate applicability effectiveness approach.