
Proceedings Paper
Evaluation and comparison of Dempster-Shafer, weighted Dempster-Shafer, and probability techniques in decision makingFormat | Member Price | Non-Member Price |
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Paper Abstract
The Monte Carlo technique is used to evaluate the performance of four techniques for making decisions in the
presence of ambiguity. A modified probability approach (both weighted and unweighted) and weighted and unweighted
Dempster-Shafer are applied to compare the reliability of these methods in producing a correct single decision based on a
priori knowledge perturbed by expert or sensor inaccuracy. These methods are tested across multiple conditions which
differ in condition mass values and the relative accuracy of the expert or sensor. Probability and weighted probability
are demonstrated to work suitably, as expected, in cases where the bulk of the input (expert belief or sensor) data can be
assigned directly to a condition or in scenarios where the ambiguity is somewhat evenly distributed across conditions.
The Dempster-Shafer approach would outperform standard probability when significant likelihood is assigned to a
particular subset of conditions. Weighted Dempster-Shafer would also be expected to outperform standard and weighted
probability marginally when significant likelihood is assigned to a particular subset of conditions and input accuracy
varies significantly. However, it is demonstrated that by making minor changes to the probability algorithm, results
similar to those produced by Dempster-Shafer can be obtained. These results are considered in light of the
computational costs of Dempster-Shafer versus probability.
Paper Details
Date Published: 13 January 2012
PDF: 5 pages
Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83502F (13 January 2012); doi: 10.1117/12.920195
Published in SPIE Proceedings Vol. 8350:
Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies
Safaa S. Mahmoud; Zhu Zeng; Yuting Li, Editor(s)
PDF: 5 pages
Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83502F (13 January 2012); doi: 10.1117/12.920195
Show Author Affiliations
Jeremy Straub, The Univ. of North Dakota (United States)
Published in SPIE Proceedings Vol. 8350:
Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies
Safaa S. Mahmoud; Zhu Zeng; Yuting Li, Editor(s)
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