中文摘要：

针对群决策分类中可解释性的推理信息存在类别错误标识的问题，提出了类别误标下证据链推理的群决策分类方法。该方法采用可信度函数的一致性和凸性，以查询案例与证据链之间的属性关联相似度作为群决策信息源的权重，建立了基于证据链推理的混合整数优化模型，实现了决策分类标识能力最大化，同时获取了可解释性最好的证据链集合。该模型考虑了决策类别的错误标识情形，依据可信度序的概念，将推导出的融合可信度作为查询案例推论可解释性的评价标准。通过多源感知数据的诊断实例，说明了该方法的有效性和合理性。

英文摘要：

Considering on the difficulty of interpretable reasoning for group decision in the mislabeled classi- fication context, a method of evidential chains （ECs）-based reasoning for group decision analysis is proposed. Based on the consistency and convexity of the belief function, association similarities between the query case and ECs on their attributes are used as the weights of the multi source information, and the mixed integer optimiza tion model of ECs-based fusion reasoning is formed to maximize the reasoning performance, achieving the most closely related ECs. For reasoning with the mislabeled instances, this framework facilitates belief preference to induce the conclusion of queries. A diagnostic experiment with multi-source sensory data verifies the efficiency and rationality of the method.