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研究多个决策者对属性有不完全类别偏好的语言案例决策方法.基于案例学习框架定义属性公共提及因子,提出考虑多重类别偏好的信息增益系数;基于灰靶决策框架建立综合靶心距最小的属性权重优化模型;基于决策者个体和群体的关联度确定决策者权重,进而给出方案排序.案例分析表明了所提出方法的应用步骤和可行性.
This paper studies the language case decision-making method of incompleteness preference of many decision-makers on attributes.According to the case-based learning framework, the public reference factor is defined and the information gain coefficient considering the preference of multiple categories is proposed. Based on the gray target decision framework, Attribute weight optimization model, decision-maker weight is determined based on the degree of association between individual and group of decision-makers, and then the program ranking is given.Case analysis shows the application steps and feasibility of the proposed method.