Evidence theory, also called belief function theory, provides an efficient tool to represent and combine uncertain information for pattern classification. Evidence combination can be interpreted, in some applications, as classifier fusion. The sources of evidence corresponding to multiple classifiers usually exhibit different classification qualities, and they are often discounted using different weights before combination. In order to achieve the best possible fusion performance, a new credal belief redistribution (CBR) method is proposed to revise such evidence. The rationale of CBR consists of transferring belief from one class not just to other classes, but also to the associated disjunctions of classes (i.e., meta-classes). As classification accuracy for different objects in a given classifier can also vary, the evidence is revised according to prior knowledge mined from its training neighbors. If the selected neighbors are relatively close to the evidence, a large amount of belief will be discounted for redistribution. Otherwise, only a small fraction of belief will enter the redistribution procedure. An imprecision matrix estimated based on these neighbors is employed to specifically redistribute the discounted beliefs. This matrix expresses the likelihood of misclassification (i.e., the probability of a test pattern belonging to a class different from the one assigned to it by the classifier). In CBR, the discounted beliefs are divided into two parts. One part is transferred between singleton classes, whereas the other is cautiously committed to the associated meta-classes. By doing this, one can efficiently reduce the chance of misclassification by modeling partial imprecision. The multiple revised pieces of evidence are finally combined by the Dempster–Shafer rule to reduce uncertainty and further improve classification accuracy. The effectiveness of CBR is extensively validated on several real datasets from the UCI repository and critically compared with that of other related fusion methods.

中文翻译：

---

基于信用信念重分布的证据组合模式分类

证据理论，也称为信念函数理论，为模式分类提供了一种表示和组合不确定信息的有效工具。在某些应用中，证据组合可以解释为分类器融合。多个分类器对应的证据来源通常表现出不同的分类质量，在组合前往往使用不同的权重进行折扣。为了实现最佳的融合性能，提出了一种新的信用信念再分配（CBR）方法来修正这些证据。CBR 的基本原理包括将信念从一个类转移到其他类，而且还转移到相关的类（即元类）。由于给定分类器中不同对象的分类精度也可能不同，证据根据从其训练邻居中挖掘的先验知识进行修订。如果选择的邻居与证据相对接近，则大量的信念将被重新分配打折。否则，只有一小部分信念会进入再分配程序。基于这些邻居估计的不精确矩阵被用来专门重新分配折扣信念。该矩阵表示错误分类的可能性（即，测试模式属于与分类器分配给它的类别不同的​​类别的概率）。在 CBR 中，折扣信念分为两部分。一部分在单例类之间转移，而另一部分则谨慎地提交给关联的元类。通过做这个，通过对部分不精确性建模，可以有效地减少误分类的机会。多条修改后的证据最终通过 Dempster-Shafer 规则进行组合，以减少不确定性并进一步提高分类精度。CBR 的有效性在来自 UCI 存储库的几个真实数据集上得到了广泛验证，并与其他相关融合方法的有效性进行了严格比较。