基于协调权的案例学习群决策分类方法

基于案例学习的决策方法具有易于理解、贴近实际决策过程的优点，成为当前决策领域的一个研究热点。文中针对如何有效地集成不同决策专家提供的案例信息，提出了一种基于协调权的案例学习群决策模型用以解决多属性分类决策问题。该方法首先针对各决策者给出不同案例数据，通过构建混合整数规划模型，识别出具有一致案例信息重要度最大化的典型案例集。然后设计了分类阈值远离程度最大化模型，以此确定兼容各个决策者案例信息的指标权重（协调权）和最优分类阈值，由此构建一致性的效用函数并应用获得的阈值进行分类决策。最后通过案例研究以及与其他模型的比较分析，验证了方法的可行性。

Case-Based Approach to Group Classification Decision-Based on Compromise Weight Setting

The case-based decision making method has the advantages of easy to understand and close to the actual decision-making process, so it becomes a hot research topic in the field of decision making. In order to effectively integrate the case information provided by different decision makers (DMs), this paper proposes a case study group decision making model based on consistent weight to solve the multiple attribute classification problem. Firstly, the method builds a mixed integer programming model based on different case data given by DMs, and identifies the typical case set with maximization of the significance degree of consistent case informaion.Then, the model of maximizing the degree away from the classification threshold is designed to determine the compromise weights and the optimal classification threshold, which is compatible with the DMs. Finally, a case study and a comparison with other methods are provided to verify the feasibility of the method.