Evaluating the reliability of sources of evidence with a two-perspective approach in classification problems based on evidence theory
Evaluating the reliability of sources of evidence with a two-perspective approach in classification problems based on evidence theory
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基于证据理论的分类问题中双视角评估证据来源的可靠性
DOI：
10.1016/j.ins.2019.08.033
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发表时间：
2020-01
影响因子：
8.1
通讯作者：
Wenhong Wei
中科院分区：
文献类型：
--
作者：
Jie Zhao;Rui Xue;Zhen-ning Dong;Jie Zhao;Wenhong Wei
冲突管理和准确性提高是基于证据理论的分类问题的两个主要关注点。基于信度评估的证据来源之间的高度冲突可以通过折现方法有效地解决。然而，这些方法可能不能保证分类模型的有效性能。为了消除高冲突，提高可靠性评估的准确性，提出了一种两视角的可靠性评估方法来生成贴现规则。独立可靠性评估（IRE）是在假设单个信号源独立工作的情况下，用来评估单个信号源的独立可靠性。另一个角度是组合可靠性评估（CRE）。通过考虑各源之间的组合关系对各源进行评价。这两种方法都被设计为监督方法，并集成了本文提出的一种新的不相似度度量——与Jousselme距离的不相似度。通过实验验证了该方法能够有效地区分证据与事实。该方法不仅对冲突管理有效，而且有助于实施正确和具体的决策，提高基于证据理论的分类模型的性能。
Abstract Conflict management and accuracy improvement are the two main concerns of classification problems based on the evidence theory. High conflict among sources of evidence can be solved effectively using discounting methods based on source-reliability evaluations. However, these methods may not ensure efficient performance of a classification model. To relieve high conflict and improve accuracy, a two-perspectives approach for reliability evaluation is presented to generate discounting rules. An independent reliability evaluation (IRE) is used to assess the independent reliability of an individual source, under the assumption that the source works independently. The other perspective is the combination reliability evaluation (CRE). It evaluates all the sources by considering the combination relationship among them. Both methods are designed as supervising methods and integrate a new dissimilarity measure proposed in this paper—decision dissimilarity—with the Jousselme distance. The ability of the new dissimilarity measure to effectively discriminate evidence from the truth can be experimentally verified. The proposed approach is not only effective for conflict management but also for the improvement of the performance of classification models based on the evidence theory as it helps implement the correct and specific decisions.
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影响因子：
1.7
作者：
G. Rota
通讯作者：
G. Rota
DOI：
--
发表时间：
2010-03
期刊：
--
影响因子：
--
作者：
John Klein;O. Colot
通讯作者：
John Klein;O. Colot
DOI：
10.1016/j.ins.2018.07.051
发表时间：
2019-04
期刊：
Inf. Sci.
影响因子：
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作者：
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通讯作者：
Durgadevi Velusamy;P. Ganeshkumar
影响因子：
5.1
作者：
Fan, XF;Zuo, MJ
通讯作者：
Zuo, MJ
DOI：
10.1145/3123266.3123341
发表时间：
2017-10
期刊：
Proceedings of the 25th ACM international conference on Multimedia
影响因子：
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作者：
Meng Liu;Liqiang Nie;M. Wang;Baoquan Chen
通讯作者：
Meng Liu;Liqiang Nie;M. Wang;Baoquan Chen