Maximizing the Information Obtained from Data Fusion
We consider the question of conjuncting data and in particular the issue of the amount of information that results. It is shown that if the data being fused is non-conflicting then the maximal information is obtained by simply taking the intersection of the data. When the data is conflicting the use of the intersection can result in the fused value having less information then any of its components. In order to maximize the resulting information in this conflicting environment some meta knowledge must be introduced to adjudicate between conflicting data. Two approaches to address this problem are introduced. The first considers using only a subset of the observations to construct the fused value, a softening of the requirement that all observations be used. The basic rational of this approach is to calculate the fused value from a subset of observations that are not to conflicting and consisting of enough of the observations to be considered a credible fusion. Central to this approach is the introduction of meta-knowledge in the form of a measure of credibility associated with the use of different subsets of the observations. The second approach is based upon the introduction of a prioritization of the observations. In this approach an observation is essentially discounted if conflicts with higher priority observations.
KeywordsSpecificity Measure Fuzzy Subset Fuzzy Measure Membership Grade Possibility Distribution
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