A belief function distance metric for orderable sets

Authors:
Zachary Sunberg;Jonathan Rogers
Affiliations:
-;-
Venue:
Information Fusion
Year:
2013

Citing 12
Cited 0

Approximations for efficient computation in the theory of evidence

Artificial Intelligence
Fuzzy Sets and Systems: Theory and Applications

Fuzzy Sets and Systems: Theory and Applications
Evidence Theory and Its Applications

Evidence Theory and Its Applications
The maximum weight hierarchy matching problem

Information Fusion
Robust combination rules for evidence theory

Information Fusion
Analyzing the degree of conflict among belief functions

Artificial Intelligence
On the plausibility transformation method for translating belief function models to probability models

International Journal of Approximate Reasoning
Distances in evidence theory: Comprehensive survey and generalizations

International Journal of Approximate Reasoning
An evidence-theoretic k-NN rule with parameter optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Assessing sensor reliability for multisensor data fusion within the transferable belief model

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The modified Dempster-Shafer approach to classification

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A proof for the positive definiteness of the Jaccard index matrix

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a new metric for characterizing conflict between belief assignments. The new metric, specifically designed to quantify conflict on orderable sets, uses a Hausdorff-based measure to account for the distance between focal elements. This results in a distance metric that can accurately measure conflict between belief assignments without saturating simply because two assignments do not have common focal elements. The proposed metric is particularly attractive in sensor fusion applications in which belief is distributed on a continuous measurement space. Several example cases demonstrate the proposed metric's performance, and comparisons with other common measures of conflict show the significant benefit of using the proposed metric in cases where a sensor's error and noise characteristics are not known precisely a priori.