Originating from the belief-rule-based (BRB) system, the extended belief rule-based (EBRB) system combined the advantages of the rule-based method and those of data-driven methods. By transforming the data set into extended belief rules and using evidential reasoning (ER), the EBRB system has expanded the application of BRB systems and demonstrated their capability in addressing classification problems. Nevertheless, the problem of activating nearly the entire rule base in every classification process is embedded in the EBRB scheme. There have been advances in rule activation for the EBRB system; however, the introduction of subjective information into the classification, high computational costs and long response times are common problems facing existing rule activation methods. To solve the problems facing rule activation for EBRB systems, a minimum centre distance rule activation (MCDRA) method for EBRB systems is proposed. In MCDRA, no subjective information is required, and no time-consuming iteration procedure is necessary. Two components of the proposed MCDRA, i.e., the filtering procedure and the selection procedure, are designed to eliminate unrelated samples of input query data and to select and activate the highly related samples to the input query data. A total of 12 benchmark data sets are used to test the performance of EBRB with MCDRA (M-EBRB). The experimental results show that compared with other rule activation methods, the proposed method obtains satisfactory rule activation ratios, accuracies and response times. Additionally, M-EBRB performs well on noisy data and comparatively with both the fuzzy-rule-based classification system (FRBCS) and several machine learning classification algorithms. In addition, MCDRA can be utilized as a generic rule activation method and can be used to optimize other rule-based classification systems.

源自基于信念规则（BRB）的系统，基于扩展信念规则（EBRB）的系统结合了基于规则的方法和数据驱动方法的优点。通过将数据集转换为扩展的置信规则并使用证据推理（ER），EBRB系统扩展了BRB系统的应用范围，并展示了其解决分类问题的能力。尽管如此，在每个分类过程中几乎激活整个规则库的问题已嵌入到EBRB方案中。EBRB系统的规则激活方面已有进展；然而，将主观信息引入分类，高计算成本和长响应时间是现有规则激活方法面临的普遍问题。为了解决EBRB系统的规则激活所面临的问题，提出了一种用于EBRB系统的最小中心距离规则激活（MCDRA）方法。在MCDRA中，不需要主观信息，也不需要耗时的迭代过程。提出的MCDRA的两个组件，即过滤过程和选择过程，旨在消除输入查询数据的不相关样本，并选择和激活与输入查询数据高度相关的样本。总共使用12个基准数据集来测试带有MCDRA（M-EBRB）的EBRB的性能。实验结果表明，与其他规则激活方法相比，该方法获得了令人满意的规则激活率，准确性和响应时间。另外，M-EBRB在嘈杂的数据上表现良好，并且与基于模糊规则的分类系统（FRBCS）和几种机器学习分类算法相比均表现出色。另外，MCDRA可用作通用规则激活方法，并可用于优化其他基于规则的分类系统。