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On the relationship between Dempster-Shafer evidence theory and Choquet integral
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010The Dempster-Shafer evidence theory and Choquet integral both are well-known information fusion methods, but they contain some natural common characteristics except apparent difference. In this paper we compare the two methods according to their objective function structures, parameter identification, and application.
Shihong Yue +3 more
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Combining Evidence in the Extended Dempster-Shafer Theory
1990The Dempster-Shafer (D-S) theory of evidence generalizes Bayesian probability theory, by providing a coherent representation for ignorance (lack of evidence). However, uncertain relationships between evidence and hypotheses bearing on this evidence are difficult to represent in applications of the theory.
Jiwen Guan +2 more
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Using Dempster-Shafer Evidence Theory for Dialog State Tracking
2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2018In a task oriented spoken dialogue system, the automatic speech recognition (ASR) and spoken language understanding (SLU) modules usually provide multiple uncertain results, which may be related or divergent. Previously, researchers used classical probability theory based approach to solve this problem, but it is difficult to handle results combination
Minglu Liu +4 more
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Epistemic Probability: the Dempster-Shafer theory of evidence
1993We mentioned in Chapter 2 the distinction between subjective and objective probability; two different interpretations of essentially the same mathematical formalism. This sharing of the use of the word ‘probability’ in the naming of both interpretations has led to an unfortunate confounding of two different concepts. The development of the mathematical
Paul Krause, Dominic Clark
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Models Based on the Dempster-Shafer Theory of Evidence
1991Like Bayesian approaches, the Dempster-Shafer theory of evidence aims to model and quantify uncertainty by degrees of belief. But in contrast to Bayesian approaches it permits assignment of degrees of belief to sets of hypotheses rather than to hypotheses in isolation.
Rudolf Kruse +2 more
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A Framework for Identity: Dempster-Shafer Theory the Flow and Combination of Evidence
2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018In this paper we present a framework for identity that is based on Barwise and Perry’s situation theory. Our focus is on identifying a culprit in a crime scene investigation based on the evidence that has been captured at the crime scene (physical evidence), and we present a framework that extends to the cyber environment. The evidence is collected and
Janelle C. Mason +3 more
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Interval Comparison Based on Dempster-Shafer Theory of Evidence
2004The problem of crisp and fuzzy interval (number) comparison is of perennial interest, because of its direct relevance in practical modeling and optimization of real-world processes under uncertainty. There are many approaches to this problem presented in literature, but in all cases the authors propose the methods which give the result of interval ...
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Facial Expression Classification Based on Dempster-Shafer Theory of Evidence
2012Facial expression recognition is a well discussed problem. Several machine learning methods are used in this regard. Among them, Adaboost is popular for its simplicity and considerable accuracy. In Adaboost, decisions are made based on the weighted majority vote of several weak classifiers.
Mohammad Shoyaib +3 more
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Enhanced Metric Learning via Dempster-Shafer Evidence Theory
2018Metric learning is a hot topic in machine learning. A proper learned metric can measure the similarity between samples better and hence significantly improves the performance of machine learning algorithm. In this paper, we propose a novel enhanced distance metric learning method via Dempster-Shafer (D-S) evidence theory.
Ying Li 0028, Yabo Zhang, Yaxin Peng
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