登普斯特-沙弗理论
熵(时间箭头)
计算机科学
辨别力
微分熵
信息论
Kullback-Leibler散度
联合熵
人工智能
数学
数据挖掘
机器学习
最大熵原理
统计
物理
哲学
认识论
量子力学
作者
Deyun Zhou,Yongchuan Tang,Jiang Wen
出处
期刊:PLOS ONE
[Public Library of Science]
日期:2017-05-08
卷期号:12 (5): e0176832-e0176832
被引量:50
标识
DOI:10.1371/journal.pone.0176832
摘要
How to quantify the uncertain information in the framework of Dempster-Shafer evidence theory is still an open issue. Quite a few uncertainty measures have been proposed in Dempster-Shafer framework, however, the existing studies mainly focus on the mass function itself, the available information represented by the scale of the frame of discernment (FOD) in the body of evidence is ignored. Without taking full advantage of the information in the body of evidence, the existing methods are somehow not that efficient. In this paper, a modified belief entropy is proposed by considering the scale of FOD and the relative scale of a focal element with respect to FOD. Inspired by Deng entropy, the new belief entropy is consistent with Shannon entropy in the sense of probability consistency. What's more, with less information loss, the new measure can overcome the shortage of some other uncertainty measures. A few numerical examples and a case study are presented to show the efficiency and superiority of the proposed method.
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