Multi-class and multi-attribute are two important features of classification problems and have different effects on the requirements and performance of the classifier. Decomposition strategy and overlap function are two effective ways to enhance the performance of classifiers, because the former decomposes a complex multi-class problem into multiple simple sub-problems; the latter uses various functions to specify the conjunctive relationship of input variables in a multi-attribute problem. Extended belief rule-based system (EBRBS) is an advanced rule-based system that has been widely used in classification problems. In order to apply decomposition strategies and overlap functions to enhance the performance of EBRBSs, the present work focuses on the investigative research and comparative evaluation of the commonly used one-versus-one (OVO) decomposition strategy and five common overlap functions to improve the performance of EBRBSs on multi-class and multi-attribute problems. More specifically, three typical kinds of EBRBSs, namely original EBRBS (O-EBRBS), EBRBS with dynamic rule activation (DRA-EBRBS), and a latest EBRBS for big data (Micro-EBRBS), are selected to conduct extensive experimental studies on twenty classification problems. To best of our knowledge, this present work is the first time to provide a meaningful and useful study in revealing the potential capability of the EBRBSs with decomposition strategy and overlap function for multi-class and multi-attribute problems. Experimental results demonstrate that the square product overlap function and the OVO strategy can enhance the performance of EBRBSs over others for twenty classification problems.

多类和多属性是分类问题的两个重要特征，对分类器的要求和性能有不同的影响。分解策略和重叠函数是提升分类器性能的两种有效方法，因为前者将复杂的多类问题分解为多个简单的子问题；后者使用各种函数来指定多属性问题中输入变量的合取关系。基于扩展信念规则的系统(EBRBS) 是一种先进的基于规则的系统，已广泛应用于分类问题。为了应用分解策略和重叠函数来提高 EBRBSs 的性能，目前的工作重点是对常用的一对一（OVO）分解策略和五种常见的重叠函数进行调查研究和比较评估，以提高性能。 EBRBSs 在多类和多属性问题上的研究。更具体地说，选择了三种典型的 EBRBS，即原始 EBRBS (O-EBRBS)、具有动态规则激活的 EBRBS (DRA-EBRBS) 和最新的大数据 EBRBS (Micro-EBRBS)，进行广泛的实验研究。二十个分类问题。据我们所知，目前的工作是第一次提供有意义和有用的研究，以揭示具有分解策略和重叠功能的 EBRBS 的潜在能力，以解决多类和多属性问题。实验结果表明，平方积重叠函数和 OVO 策略可以提高 EBRBS 对 20 个分类问题的性能。