Consid{é}rant la d{é}pendance dans la th{é}orie des fonctions de croyance
This work addresses a specific issue in belief function theory for researchers in uncertainty reasoning, but it appears incremental as it builds on existing combination rules and independence assumptions.
The paper tackles the problem of selecting appropriate combination rules for aggregating beliefs in the theory of belief functions by proposing to learn source independence, and it introduces a measure of independence to integrate into mass functions before combination, though no concrete numerical results are provided.
In this paper, we propose to learn sources independence in order to choose the appropriate type of combination rules when aggregating their beliefs. Some combination rules are used with the assumption of their sources independence whereas others combine beliefs of dependent sources. Therefore, the choice of the combination rule depends on the independence of sources involved in the combination. In this paper, we propose also a measure of independence, positive and negative dependence to integrate in mass functions before the combinaision with the independence assumption.