Combining partially independent belief functions
This work addresses uncertainty management in belief function theory for applications relying on multiple information sources, representing an incremental advancement.
The paper tackles the problem of aggregating uncertain information from multiple sources by introducing a method to quantify sources' degree of independence and proposing a new combination rule that incorporates this degree, demonstrated on generated mass functions.
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.