Abstract A belief rule mining approach is proposed to generate belief rules with a customized set of criteria by mining from multiple belief rules that are trained using data with varied sets of criteria. As the theoretical basis of the belief rule mining approach, the key concepts are defined, including the weights and reliabilities of cases, criteria, models, and belief rules. Based on the key concepts, multiple sub-models composed of belief rules with varied sets of criteria are initialized and optimized. Then, the optimized sub-models are integrated using the evidential reasoning rule into belief rules with a customized set of criteria. In the belief rule mining process, the weights and reliabilities of the sub-models are considered according to the weight and reliability calculation procedures of models proposed in this study. The proposed approach is used to help diagnose thyroid nodules with 527 medical cases, in which its applicability is demonstrated. By comparative experiments, the diagnostic correctness of the proposed approach is verified to be higher than those of the directly-optimized model and the approach without the consideration of reliability.

中文翻译：

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基于证据推理规则的医学诊断信念规则挖掘

摘要 提出了一种信念规则挖掘方法，通过从使用具有不同标准集的数据训练的多个信念规则中挖掘来生成具有自定义标准集的信念规则。作为信念规则挖掘方法的理论基础，定义了关键概念，包括案例的权重和可靠性、标准、模型和信念规则。基于关键概念，初始化和优化由具有不同标准集的信念规则组成的多个子模型。然后，使用证据推理规则将优化的子模型集成到具有自定义标准集的信念规则中。在置信规则挖掘过程中，根据本研究提出的模型的权重和可靠性计算程序，考虑子模型的权重和可靠性。所提出的方法用于帮助诊断 527 个医学病例的甲状腺结节，并证明了其适用性。通过对比实验，验证了该方法的诊断正确性高于直接优化模型和不考虑可靠性的方法。