Int{é}gration d'une mesure d'ind{é}pendance pour la fusion d'informations
This work addresses a specific issue in data fusion for applications dealing with uncertainty, but it appears incremental as it builds on existing belief function theory.
The paper tackles the problem of improving decision-making under uncertainty and imprecision in data fusion by proposing an approach that incorporates an independence measure before combining information, specifically within the theory of belief functions.
Many information sources are considered into data fusion in order to improve the decision in terms of uncertainty and imprecision. For each technique used for data fusion, the asumption on independance is usually made. We propose in this article an approach to take into acount an independance measure befor to make the combination of information in the context of the theory of belief functions.