Combining partially independent belief functions

TL;DR

This paper introduces a method to quantify source independence in belief functions and proposes a new combination rule that considers this independence, improving how opinions from multiple sources are aggregated.

Contribution

It presents a novel approach to measure source independence and a new combination rule that adapts based on this measure, enhancing belief function aggregation.

Findings

Quantifies sources' independence degree effectively.

Demonstrates improved combination rule performance.

Illustrates method on generated mass 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.