Data fusion: a study of adaptative combinations with incomplete certainty qualification: Part II: local versus global certainty qualification

Abstract

Purpose

The first part of this issue investigated the properties of the adaptive rule initially proposed by Dubois and Prade given in the framework of possibility theory, when the certainty qualification is rather expressed in more general t‐norms and t‐conorms connectives. This led to two new family of adaptive rules expressed using residual implication and t‐conorm connective, respectively. The problem of addressing uncertain inputs has also been examined and a waved decomposition has been proposed in PII we study adaptative combinations with incomplete certainty qualification. However, another problem that arises when combining uncertain inputs consists of the relationship between the certainty attached to the inputs and the certainty attached to the output, conceptualized by the resulting distribution when using adaptive combination rule. In other words, how does the combination rule improves or deteriorates the certainty of the overall system? This paper seeks to address this issue.

Design/methodology/approach

This paper fully addresses this issue and attempts to evaluate the combination rule from the certainty viewpoint attached to the result in comparison to initial certainty values attached to the inputs.

Findings

Especially, it has been proven that under certain hypotheses, the rule allows the user to hide the local certainties attached to the initial inputs, while highlighting only the certainty due to the lack of consistency among the sources.

Originality/value

New functional adaptative rules are put forward based on residual implicators and t‐conorm operators.

Citation

Oussalah, M. (2006), "Data fusion: a study of adaptative combinations with incomplete certainty qualification: Part II: local versus global certainty qualification", Kybernetes, Vol. 35 No. 10, pp. 1607-1627. https://doi.org/10.1108/03684920610688603