Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2020-06-02 , DOI: 10.1016/j.knosys.2020.106096 Haizhen Zhu , Mingqing Xiao , Xin Zhao , Xilang Tang , Longhao Yang , Weijie Kang , Zhaozheng Liu
The widely applied belief-rule-based(BRB) system has demonstrated its advantages in handling both qualitative and quantitative information. As an extension of BRB system, the extended belief-rule-based(EBRB) system bridges the rule-based methods and data-driven methods by efficiently transforming data into extended belief rules(EBRs). Many works have been done to apply EBRB system in addressing classification problems. However, the problems of making use of all attributes indiscriminately and activating almost all EBRs still affect the accuracy and computational efficiency of EBRB system. In this paper, a structure optimization method for EBRB(SO-EBRB) system, including attribute optimization and rule activation, is proposed to address aforementioned problems. In the attribute optimization, a weighted minimum redundancy maximum relevance(MRMR) method is proposed, where the relevance between attributes and label as well as the redundancy among attributes are used to evaluate attributes. Afterwards, the proposed attribute weight calculation method is utilized to assign attribute weights for the EBRB system. In rule activation, an improved minimum centre distance rule activation(MCDRA) method, which considering the weights of attributes in distance calculation, is used to activate customized EBRs for input query data. 15 benchmark classification data sets are utilized to verify the effectiveness of the proposed SO-EBRB method. The results show that, compared with conventional EBRB system, the SO-EBRB system achieves higher classification accuracy, lower rule activation ratio and less response time. Additionally, comparison between the proposed method and some state-of-art machine learning algorithms demonstrates that the SO-EBRB system achieves prominent performance in addressing classification problems.
中文翻译:
基于扩展信念规则的分类系统的结构优化方法
广泛应用的基于信任规则的系统已证明其在处理定性和定量信息方面的优势。作为BRB系统的扩展,扩展的基于信任规则的系统(EBRB)通过将数据有效地转换为扩展的信任规则(EBR),将基于规则的方法和数据驱动的方法联系起来。为了解决分类问题,已经进行了许多工作来应用EBRB系统。但是,不加选择地利用所有属性和激活几乎所有EBR的问题仍然影响着EBRB系统的准确性和计算效率。针对上述问题,本文提出了一种针对EBRB(SO-EBRB)系统的结构优化方法,包括属性优化和规则激活。在属性优化中,提出了一种加权最小冗余最大相关性(MRMR)方法,该方法利用属性与标签之间的相关性以及属性之间的冗余性来评价属性。然后,利用提出的属性权重计算方法为EBRB系统分配属性权重。在规则激活中,一种改进的最小中心距离规则激活(MCDRA)方法在距离计算中考虑了属性的权重,用于为输入查询数据激活定制的EBR。利用15个基准分类数据集来验证所提出的SO-EBRB方法的有效性。结果表明,与传统的EBRB系统相比,SO-EBRB系统具有更高的分类精度,更低的规则激活率和更少的响应时间。另外,