A Classification Decision Making Method for Large Groups Based on Personalized Parameter Learning Under Social Network Context
In order to solve the problem of large group classification decision-making in social network environment,firstly,we construct a personalized similarity thresh-old learning model,integrate the similarity with social network,and get the modified social network;then,we use sub-network segmentation algorithm to group decision-makers,and compute the group preferences of subgroups through DeGroot model;next,we integrate the degree of consistency of the preference order,cohesion of sub-groups and the number of their members in the process of aggregation of group preferences,and compute the optimal weight assignment of three indicators through parameter learning,and then compute the weights of subgroups.Secondly,in the process of group preference aggregation,we combine the degree of preference order consistency,subgroup cohesion and its number of members,and calculate the optimal weight assignment of the three indexes through parameter learning to compute the subgroup weights.Finally,the validity and feasibility of the method are verified by an example.
Social networktrust relationshipmultiple indicatorspersonalized sim-ilarity threshold
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