融合模糊推理与贝叶斯网络，提升多目标群体决策的准确性和一致性。
A Study on Group Decision Making Problem Based on Fuzzy Reasoning and Bayesian Networks
- 用模糊规则库处理专家语言和量纲差异问题。
- 构建分层贝叶斯网络，动态优化概率表，捕捉多维指标非线性关系。
- 在学生评价中实现86.0%分类准确率，F1提升53.4%，适合复杂决策场景。
针对具有多目标属性的群体决策问题，本文提出一种融合模糊推理与贝叶斯网络的决策系统。通过结合阈值、隶属函数、专家经验与领域知识构建模糊规则库，解决量纲差异与专家语言变量等量化难题。设计分层贝叶斯网络，节点由专家选定，采用最大似然估计动态优化条件概率表，建模多维指标间的非线性关联，实现后验概率聚合。在综合学生评价案例中，该方法与传统加权评分法对比，表现出更优的规则构建能力与排序一致性，分类准确率达86.0%，F1值提升53.4%。此外，在多个真实世界数据集上的计算实验验证了方法在不同群体决策场景下的性能与鲁棒性，证明其可靠性。
原文摘要 · Abstract (English)
Aiming at the group decision - making problem with multi - objective attributes, this study proposes a group decision - making system that integrates fuzzy inference and Bayesian network. A fuzzy rule base is constructed by combining threshold values, membership functions, expert experience, and domain knowledge to address quantitative challenges such as scale differences and expert linguistic variables. A hierarchical Bayesian network is designed, featuring a directed acyclic graph with nodes selected by experts, and maximum likelihood estimation is used to dynamically optimize the conditional probability table, modeling the nonlinear correlations among multidimensional indices for posterior probability aggregation. In a comprehensive student evaluation case, this method is compared with the traditional weighted scoring approach. The results indicate that the proposed method demonstrates effectiveness in both rule criterion construction and ranking consistency, with a classification accuracy of 86.0% and an F1 value improvement of 53.4% over the traditional method. Additionally, computational experiments on real - world datasets across various group decision scenarios assess the method's performance and robustness, providing evidence of its reliability in diverse contexts.
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