Name
Title | ||
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A Scheme For Constructing Evidence Structures In Dempster-Shafer Evidence Theory For Data Fusion |
Abstract | ||
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This paper addresses the issue of evidence structure construction involved in the Dempster-Shafer evidence theory (DSET) based reasoning for data fusion. An in-depth study is carried out on the properties of the proposed Proportional Difference Evidence Structure Constructing Scheme (PDESCS). Some properties have been mathematically proved for the PDESCS associated DSET. If PDESCS is applied to probabilistic evidence, in terms of posterior probability distributions, the DSET based reasoning with the maximum commonality decision making scheme is equivalent to the Beyesian approach with the maximum aposteriori probability principle (MAP). If PDESCS is applied to fuzzy evidence, in terms of fuzzy sets, the DSET based reasoning is equivalent to applying the maximum fuzzy membership decision making scheme to the intersected fuzzy set by the product T-norm operator. If PDESCS is applied to both probabilistic evidence and fuzzy evidence, the DSET based reasoning is equivalent to applying the maximum fuzzy set probability decision making scheme. To show the effectiveness of the PDESCS associated DSET, experiments are carried out for classifying human brain MR (magnetic resonance) images. It is concluded that the proposed scheme works well, and provides not only a unified framework to accommodate probabilistic evidence and fuzzy evidence, but also an effective reasoning mechanism to deal with different uncertainty, in terms of randomness and fuzziness, as well as precision. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/CIRA.2003.1222309 | 2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS |
Keywords | Field | DocType |
fuzzy sets,histograms,sensor fusion,bayesian approach,posterior probability,fuzzy set,probability distributions,probability,dempster shafer,uncertainty,magnetic resonance image,case based reasoning,data engineering,probability distribution,fuzzy set theory,maximum a posteriori estimation,data fusion | Computer science,Fuzzy logic,Posterior probability,Sensor fusion,Fuzzy set,Artificial intelligence,Maximum a posteriori estimation,Probabilistic logic,Case-based reasoning,Dempster–Shafer theory,Machine learning | Conference |
Citations | PageRank | References |
9 | 0.74 | 1 |
Authors | ||
2 | ||