Abstract Rule reduction is one of the focuses of numerous researches on belief-rule-based system, in some cases, too many redundant rules may be a concern to the rule-based system. Though rule reduction methods have been widely used in the belief-rule-based system, extended belief-rule-based system, which is an expansion of belief-rule-based system, still lacks methods to reduce and train rules in the extended belief rule base (EBRB). To this end, this paper proposes an EBRB reduction and training method. Based on the density-based spatial clustering applications with noise (DBSCAN) algorithm, a new EBRB reduction method is proposed, where all the rules in the EBRB will be visited and rules within the distance of the fusion threshold will be fused. Moreover, the EBRB training method using parameter learning, which uses a set of training data to train the parameters of EBRB, is also proposed to improve the accuracy of the EBRB system. Two case studies of regression and classification are used to illustrate the feasibility and efficiency of the proposed EBRB reduction and training method. Comparison results show that the proposed method can effectively downsize the EBRB and increase the accuracy of EBRB system.

中文翻译：

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一种基于DBSCAN算法的扩展置信规则库的规则约简与训练新方法

摘要 规则约简是众多基于信念规则的系统研究的重点之一，在某些情况下，过多的冗余规则可能会成为基于规则系统的关注点。尽管规则约简方法在基于信念规则的系统中得到了广泛的应用，但扩展的基于信念规则的系统作为基于信念规则的系统的扩展，仍然缺乏对扩展信念规则中的规则进行约简和训练的方法碱 (EBRB)。为此，本文提出了一种减少EBRB的方法和训练方法。在基于密度的带噪声空间聚类应用（DBSCAN）算法的基础上，提出了一种新的EBRB约简方法，即访问EBRB中的所有规则并对融合阈值范围内的规则进行融合。此外，使用参数学习的EBRB训练方法，它使用一组训练数据来训练 EBRB 的参数，也被提出来提高 EBRB 系统的准确性。回归和分类的两个案例研究被用来说明所提出的 EBRB 减少和训练方法的可行性和效率。对比结果表明，所提出的方法可以有效地缩小EBRB的规模，提高EBRB系统的精度。