Combining partially independent belief functions

Abstract : The theory of belief functions manages uncertainty and also proposes a set of combination rules to aggregate opinions of several sources. Some combination rules mix evidential information where sources are independent; other rules are suited to combine evidential information held by dependent sources. In this paper we have two main contributions: First we suggest a method to quantify sources' degree of independence that may guide the choice of the more appropriate set of combination rules. Second, we propose a new combination rule that takes consideration of sources' degree of independence. The proposed method is illustrated on generated mass functions.
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Article dans une revue
Decision Support Systems, Elsevier, 2015, pp.37-46. 〈10.1016/j.dss.2015.02.017〉
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Soumis le : mardi 17 mars 2015 - 14:57:25
Dernière modification le : jeudi 15 novembre 2018 - 11:58:50
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Mouna Chebbah, Arnaud Martin, Boutheina Ben Yaghlane. Combining partially independent belief functions. Decision Support Systems, Elsevier, 2015, pp.37-46. 〈10.1016/j.dss.2015.02.017〉. 〈hal-01132564〉



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