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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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Submitted on : Tuesday, March 17, 2015 - 2:57:25 PM
Last modification on : Friday, March 6, 2020 - 4:10:03 PM
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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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