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Data classification using evidence reasoning rule

机译：使用证据推理规则进行数据分类

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In Dempster-Shafer evidence theory (DST) based classifier design, Dempster's combination (DC) rule is commonly used as a multi-attribute classifier to combine evidence collected from different attributes. The main aim of this paper is to present a classification method using a novel combination rule i.e., the evidence reasoning (ER) rule. As an improvement of the DC rule, the newly proposed ER rule defines the reliability and weight of evidence. The former indicates the ability of attribute or its evidence to provide correct assessment for classification problem, and the latter reflects the relative importance of evidence in comparison with other evidence when they need to be combined. The ER rule-based classification procedure is expatiated from evidence acquisition and estimation of evidence reliability and weight to combination of evidence. It is a purely data-driven approach without making any assumptions about the relationships between attributes and class memberships, and the specific statistic distributions of attribute data. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show high classification accuracy that is competitive with other classical and mainstream classifiers. (C) 2016 Elsevier B.V. All rights reserved.
机译：在基于Dempster-Shafer证据理论（DST）的分类器设计中，Dempster的组合（DC）规则通常用作多属性分类器，以合并从不同属性收集的证据。本文的主要目的是提出一种使用新颖的组合规则即证据推理（ER）规则的分类方法。作为DC规则的改进，新提出的ER规则定义了证据的可靠性和重要性。前者表示属性或其证据对分类问题提供正确评估的能力，而后者则表示在需要合并时，证据与其他证据相比的相对重要性。基于ER规则的分类程序从证据获取，证据可靠性和权重估计到证据组合阐述。它是一种纯粹的数据驱动方法，无需对属性和类成员关系以及属性数据的特定统计分布进行任何假设。来自加州大学欧文分校（UCI）机器学习数据库的五个流行基准数据库的实验结果表明，其分类准确率很高，与其他经典分类器和主流分类器相比，具有竞争力。 （C）2016 Elsevier B.V.保留所有权利。

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