Source Independence in the Theory of Belief Functions

Abstract : The theory of belief functions manages uncertainty and proposes a set of combination rules to aggregate beliefs 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. Information on sources' independence is required to justify the choice of the adequate type of combination rules. In this thesis, we suggest a method to quantify sources' degrees of independence that may guide the choice of the appropriate type of combination rules. In fact, we propose a statistical approach to learn sources' degrees of independence from all provided evidential information. There are three main uses of estimating sources' degrees of independence: First, we use sources' degree of independence to guide the choice of combination rules to use when aggregating beliefs of several sources. Second, we propose to integrate sources' degrees of independence into sources' beliefs leading to an operator similar to the discounting. Finally, we define a new combination rule weighted with sources' degree of independence.
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  • HAL Id : tel-01373044, version 1

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Mouna Chebbah. Source Independence in the Theory of Belief Functions. Computer Science [cs]. Université de Rennes 1 [UR1]; Université de Tunis, 2014. English. ⟨tel-01373044⟩

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