Uncertainty Modeling in Risk Assessment Based on Dempster–Shafer Theory of Evidence with Generalized Fuzzy Focal Elements
By P. Dutta. 2015
65 citations recorded by the source corpus.
Abstract Dempster–Shafer theory of evidence is one of the important tools for decision making under uncertainty. It is more useful in situations when cost of technical difficulties is involved or uniqueness of the situation under study makes it difficult/impossible to cover enough observations to quantify the models with real data. Consequently, experts provide opinions in terms of basic probability assignment for focal elements. Usually, it is seen that experts provide basic probability assignment for interval (or crisp) focal elements. However, due to presence of uncertainty focal elements can sometimes be treated as normal/generalized triangular fuzzy number (TFN in short) instead of intervals or crisp sets. TFN encodes only most likely value (mode) and the spread. This paper presents an attempt to combine Dempster–Shafer structures (DSS in short) with generalized/normal fuzzy focal elements using possibilistic sampling technique. To this end, human health risk assessment is carried out under such setting.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c0f8af29ef0d475c0ddfb1e18286812d63b34d35
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In Artificial Intelligence
In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy (2019, background): “…Shafer evidence theory is widely applied in various fi elds of information fusion, such as decision making [50 – 55], pattern recognition [56 – 58], risk analysis [59,60], human reliability analysis [61], supplier selection [62], aphasia diagnosis [63], fault diagnosis [64 – 67], and so on [68,69].”