实体异构性下证据链融合推理的多属性群决策

Heterogeneous Evidence Chains Based Fusion Reasoning for Multi-attribute Group Decision Making

针对多属性群决策中可解释性证据融合推理的实体异构性问题,给出了一个实体异构性下证据链融合推理的多属性群决策方法.基于证据推理理论,引入证据链关联的概念,从多数据表提供的数据矩阵中获取可区分的近邻证据集,推导了各数据表的相似度矩阵,并构建半正定矩阵的二次优化模型,共享群决策专家的经验知识.使用Dempster正交规则,论证了异构实体之间可解释性推理中可信度融合的合理性,并使用证据融合规则集成各个数据表的近邻证据中获得的可信度,验证了调和多源异构数据中不一致信息的有效性.通过具有实体异构性的心脏病多决策数据诊断实例说明了方法的可行性与合理性.

In multi-attribute group decision making, the heterogeneity of entities causes a lot di-culties for the inter-pretable evidence fusion reasoning process, thus a novel heterogeneous evidential chains based fusion reasoning （Hefur） method is proposed for multi-attribute group decision making. Based on the theory of evidential reasoning, the concept of evidential chain association is introduced to obtain the nearest neighbor set of distinct evidences from the data matrix of multiple decision tables. Similarity matrices are derived from data tables, and positive semi-definite matrix quadratic optimization model is built to share, sharing the experience knowledge of the group decision-making experts. Using the Dempster’s quadrature rule, the rationality of the belief integrating is verified in the interpretable reasoning process with heterogeneous entities, and the combined belief is obtained from nearest neighbor evidences for each data table using the evidence fusion rules. Moreover, the validity is verified for dealing with the harmonic information inconsistence of the multi-heterogeneous data sources. Numerical experiments on the heart disease diagnosis with entity heterogeneity illustrate the feasibility and rationality of the proposed method.