Research On Three-way Decision Method Of Interval-valued Hesitation Fuzzy Set

Different from the traditional hesitant fuzzy set model which can only deal with the situation of precise evaluation value,interval hesitant fuzzy set is a new extended form of fuzzy set,which can be used to deal with the decision-making problem of interval evaluation information.The rough set model only uses a binary relationship to express knowledge,which obviously has limitations in the processing of complex information,but the multi-granularity rough set can be applied to solve multi-dimensional uncertain information with the help of multiple binary relationships.Most of the current interval hesitant fuzzy multi-attribute decision-making methods can only be used to select the optimal strategy and cannot be applied to decision-making in complex situations.The three-way decision uses a pair of thresholds to divide the domain of discourse into three different areas with no intersection,which also coincides with the situation when people usually make decisions.For the problem that the effective information cannot be obtained directly from the interval hesitant fuzzy decision information system,this thesis introduces the multi-granularity rough set into the system,and proposes the multigranularity interval hesitant fuzzy rough set to deal with fuzzy data.In order to make decision-making judgments on the objects in the interval hesitant fuzzy decision information system,combined with the three-way decision theory,two three-way decision models are proposed.The specific research contents are as follows:1.Combined with multi-granulation rough set theory,thesis introduces two threeway decision models based on multi-granulation interval-valued hesitant fuzzy rough set.First of all,by employing an interval-valued hesitant fuzzy relation,it is defined that a pair of lower and upper interval-valued hesitant fuzzy rough approximation operators.It is proposed that rough interval-valued hesitant fuzzy sets and two multi-granulation rough interval-valued hesitant fuzzy set models.Then,their properties are discussed.Secondly,we define the combination formula for the upper and lower approximations of multigranulation rough interval-valued hesitant fuzzy sets,and present a new interval-valued hesitant fuzzy continuous cross-entropy.Then,the conditional probabilities under four cases are calculated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)approach.Thirdly,the thresholds in interval-valued hesitant fuzzy decision-theoretic rough sets are calculated,and relevant three-way decision rules are given.Finally,based on the proposed multi-granularity interval-valued hesitant fuzzy rough set,two kinds of three-way decision models are constructed and corresponding decision rule extraction algorithms are designed.Experiments with cases and multiple different UCI data sets show that the proposed model is effective for the target.The evaluation can take different attitudes and give a decision-making scheme,which verifies its effectiveness.2.The definition of interval-valued hesitation fuzzy distance is given,and based on this,a sequential three-way decision model in the context of interval-valued hesitation fuzzy is proposed.First of all,considering that interval-valued hesitation fuzzy number composed of multiple possible interval membership degrees’ set cannot be immediately contrasted with thresholds,it is necessary to be converted to a distance before comparing.Then,in order to avoid the subjectivity of the selection of thresholds,the threshold is reasonably calculated through the shadow set.By use of the interval-valued hesitation fuzzy distance,new decision-making rules are formulated.Based on this,a dynamic update mechanism of sequential three-way decision is established for the changes in attribute values.Finally,interview evaluation instances and simulation experiments are used to verify the feasibility and effectiveness of the above model.