General Combination Rules For Qualitative And Quantitative Beliefs


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General Combination Rules for Qualitative and Quantitative Beliefs


General Combination Rules for Qualitative and Quantitative Beliefs

Author: ARNAUD MARTIN

language: en

Publisher: Infinite Study

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Martin and Osswald have recently proposed many generalizations of combination rules on quantitative beliefs in order to manage the conflict and to consider the specificity of the responses of the experts. Since the experts express themselves usually in natural language with linguistic labels, Smarandache and Dezert have introduced a mathematical framework for dealing directly also with qualitative beliefs. In this paper we recall some element of our previous works and propose the new combination rules, developed for the fusion of both qualitative or quantitative beliefs.

A belief combination rule for a large number of sources


A belief combination rule for a large number of sources

Author: Kuang Zhou

language: en

Publisher: Infinite Study

Release Date:


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In this paper, based on the assumption that the majority of sources are reliable, a combination rule for a large number of sources is proposed using a simple idea: the more common ideas the sources share, the more reliable these sources are supposed to be. This rule is adaptable for aggregating a large number of sources which may not all be reliable. It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement. The mass on the empty set will be kept as an indicator of the conflict.

Belief Functions: Theory and Applications


Belief Functions: Theory and Applications

Author: Fabio Cuzzolin

language: en

Publisher: Springer

Release Date: 2014-09-05


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This book constitutes the thoroughly refereed proceedings of the Third International Conference on Belief Functions, BELIEF 2014, held in Oxford, UK, in September 2014. The 47 revised full papers presented in this book were carefully selected and reviewed from 56 submissions. The papers are organized in topical sections on belief combination; machine learning; applications; theory; networks; information fusion; data association; and geometry.