Int{\'e}gration d'une mesure d'ind{\'e}pendance pour la fusion d'informations

TL;DR

This paper introduces a method to incorporate an independence measure into data fusion processes within the belief functions framework, aiming to enhance decision accuracy under uncertainty.

Contribution

It proposes a novel approach to account for independence measures prior to information combination in belief function theory.

Findings

Improved decision accuracy in data fusion scenarios.

Effective incorporation of independence measures into belief functions.

Enhanced handling of uncertainty and imprecision.

Abstract

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.