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Approximation algorithms and decision making in the Dempster-Shafer theory of evidence — An empirical study

Mathias Bauer
1997 International Journal of Approximate Reasoning  
It describes an empirical study that examines the appropriateness of these approximation procedures in decision-making situations.  ...  The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the major points of criticism this formalism has to face.  ...  I wish to thank my colleagues Dietmar Dengler and Harald Feibel for providing helpful comments on earlier versions of this paper, Patrick Brandmeier for his support in implementing the testbed and running  ... 
doi:10.1016/s0888-613x(97)00013-3 fatcat:us2ppk3sijcyjghhsufudbahm4

Prediction of financial distress: An empirical study of listed Chinese companies using data mining

Ruibin Geng, Indranil Bose, Xi Chen
2015 European Journal of Operational Research  
The deterioration in profitability of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss.  ...  We observe that the performance of neural networks is more accurate than other classifiers, such as decision trees and support vector machines, as well as an ensemble of multiple classifiers combined using  ...  classifiers based on Dempster-Shafer evidence theory Li and Sun (2009) No No Yes Yes Yes No No Yes ST Chinese listed companies Hybrid case based reasoning system Chen and Du (2009) Yes No No No No No  ... 
doi:10.1016/j.ejor.2014.08.016 fatcat:7r5ctbqufvfgfpdxqa3dvi7fku

Special issue on the 1996 uncertainty in AI (UAI'96) conference—preface

Piero P. Bonissone
1997 International Journal of Approximate Reasoning  
Every year, the Uncertainty in Artificial Intelligence (UAI) Conference provides a highly selected forum in which the most important innovations in the field of reasoning with uncertain, imprecise, and  ...  As the editor-in-chief of IJAR, I encouraged some of the authors of papers presented at UAI'96 to extend their original material, incorporating the feedback received during the conference, and presenting  ...  Bauer, in his paper "Approximation Algorithms and Decision Making in the Dempster-Shafer Theory of Evidence--an Empirical Study," covers the issue of computational complexity of reasoning based on Dempster-Shafer  ... 
doi:10.1016/s0888-613x(97)00022-4 fatcat:lq2iblwdsbdshcb3btkrsr7mau

An Advanced Decision Support System for Medical Diagnosis

Ioan Dumitrache, Ioana Mihu, Monica C. Voinescu
2008 IFAC Proceedings Volumes  
This paper presents a clinical diagnosis support system which combines the advantages of Dempster-Shafer theory with Bayesian networks in order to simulate the uncertain medical reasoning.  ...  We propose a hierarchical structure using Dempster-Shafer at the upper level for evaluating more general hypothesis (disease groups) and Bayesian networks at the lower level for a more accurate analysis  ...  There are numerous studies which tackle this problem using various approximate algorithms, such as the solution presented in Wemmenhove et al. [2007] .  ... 
doi:10.3182/20080706-5-kr-1001.01625 fatcat:75ghfx2btzdz7jpsxzz77wqrei

Approximations for Decision Making in the Dempster-Shafer Theory of Evidence [article]

Mathias Bauer
2013 arXiv   pre-print
Besides introducing a new algorithm using this method, this paper describes an empirical study that examines the appropriateness of these approximation procedures in decision making situations.  ...  The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the main points of criticism this formalism has to face.  ...  I'd like to thank my colleagues Dietmar Dengler and Harald Feibel for their comments on earlier versions of this paper and Patrick Brandmeier for his support in implementing the testbed and running the  ... 
arXiv:1302.3557v1 fatcat:d4yaaft5ujawbppi3ounyhgmzq

DC Proposal: Decision Support Methods in Community-Driven Knowledge Curation Platforms [chapter]

Razan Paul
2011 Lecture Notes in Computer Science  
More specifically, we devise techniques for semi-automated diagnosis and key disease feature inferencing from an existing pool of patient cases -that are shared and discussed in the SKELETOME community-driven  ...  Performing efficient and automated knowledge discovery in this domain poses serious challenges, one of the main issues being the lack of a proper formalization.  ...  The work presented in this paper is supported by the Australian Research Council (ARC) under the Linkage grant SKELETOME -LP100100156.  ... 
doi:10.1007/978-3-642-25093-4_26 fatcat:odrqyckyevb2ljysbw2awv76nu

Review of Uncertainty Reasoning Approaches as Guidance for Maritime and Offshore Safety-Based Assessment

J. Liu, J.B. Yang, J. Wang, H.S. Sii
2002 The journal of the Safety and Reliability Society  
In this paper we review some of the most important ones, i.e., Bayesian theory of probability, Dempster-Shafer theory of evidence, and fuzzy set theory, describe how they work and in what ways they differ  ...  The study is intended to provide guidance in the process of developing frameworks for safety-based decision analysis using different methods for reasoning under uncertainty.  ...  Acknowledgements This work forms part of the projects supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant References GR/R30624 and GR/R32413.  ... 
doi:10.1080/09617353.2002.11690751 fatcat:2dx7qpb2yng3vd3fzhq44dl7da

Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020) [article]

Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain, Abbas Khosravi, Afsaneh Koohestani, Mohammad Hossein Zangooei, Moloud Abdar, Adham Beykikhoshk, Afshin Shoeibi, Assef Zare, Maryam Panahiazar, Saeid Nahavandi (+3 others)
2020 arXiv   pre-print
Proper quantification of uncertainty provides valuable information for optimal decision making.  ...  This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques.  ...  Data availability The authors declare that all data supporting the findings of this study are available within the paper and its Supplementary Information.  ... 
arXiv:2008.10114v1 fatcat:pgiep5djj5bdpe7qr4n2f4buky

Generalization of the Dempster-Shafer theory: a fuzzy-valued measure

C. Lucas, B.N. Araabi
1999 IEEE transactions on fuzzy systems  
The Dempster-Shafer theory (DST) may be considered as a generalization of the probability theory, which assigns mass values to the subsets of the referential set and suggests an interval-valued probability  ...  Index Terms-Dempster-Shafer theory, fuzzy body of evidence, fuzzy generalization of the Dempster-Shafer theory, fuzzy set of consistent probability measures, fuzzy valued belief function, fuzzy valued  ...  Dempster-Shafer Theory T HE Dempster-Shafer theory (DST) could be considered as a generalization of the probability theory [1] - [3] .  ... 
doi:10.1109/91.771083 fatcat:g5jdaxwgdjestizaapugfq4htu

Updating Ambiguous Beliefs

Itzhak Gilboa, David Schmeidler
1993 Journal of Economic Theory  
Among several ways to update ambiguous beliefs proposed in the literature, we consider the Dempster-Shafer updating rule (Dempster (1968) and Shafer (1976)) and the full Bayesian updating rule (Fagin and  ...  Halpern (1991) and Ja¤ray (1992)), and argue that the Dempster-Shafer updating rule rather than the full Bayesian updating rule better matches with an econometrician's common adoption of the analogy principle  ...  on partial identi…ed model, and (ii) to provide decision theorists some outside-lab evidence that the econometrician's common way of updating ambiguous beliefs is in line with the Dempster-Shafer updating  ... 
doi:10.1006/jeth.1993.1003 fatcat:vpovrtg5rzb43jfidhd6kbgqxa

Faithworthy Collaborative Spectrum Sensing Based on Credibility and Evidence Theory for Cognitive Radio Networks

Fang Ye, Xun Zhang, Yibing Li, Chunrui Tang
2017 Symmetry  
In recent years, plenty of algorithms have been proposed owing to the unique advantages of the Dempster-Shafer (D-S) theory of evidence in terms of uncertainty representation [14] [15] [16] [17] [18] [  ...  In [14] , Dempster-Shafer theory is first applied in the data fusion of CSS.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym9030036 fatcat:2lczqt7o2fd5xn7qyyq2igq6g4

WPT-ANN and Belief Theory Based EEG/EMG Data Fusion for Movement Identification

Fazia Sbargoud, Mohamed Djeha, Mohamed Guiatni, Noureddine Ababou
2019 Traitement du signal  
The electromyography (EMG) and electroencephalography (EEG) are two frequently used modalities of bio-signals in the field of bio-robotics.  ...  To solve the problem, this paper proposes an EEG/EMG data fusion method that take advantages of both signals and overcome their drawbacks to achieve accurate identification of movements.  ...  Shafer is an extension of Dempster's idea using the Bayesian probabilities [26] . This theory is commonly referred to as Dempster-Shafer Theory (DST).  ... 
doi:10.18280/ts.360502 fatcat:kyv4gxhhdjgdbim6lkt3dqpo2a

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain, Abbas Khosravi, Afsaneh Koohestani, Mohammad Hossein Zangooei, Moloud Abdar, Adham Beykikhoshk, Afshin Shoeibi, Assef Zare, Maryam Panahiazar, Saeid Nahavandi (+3 others)
2021 Annals of Operations Research  
Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.  ...  This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques.  ...  Dempster-Shafer theory (DST) P. Dempster and his student Glenn Shafer introduced Dempster Shafer Theory (Denźux, 2016) . The theory tried to overcome the limitations of Bayesian methods.  ... 
doi:10.1007/s10479-021-04006-2 pmid:33776178 pmcid:PMC7982279 fatcat:wq2eweu7hfduvbhwo5oaqaltoe

Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory

Rahul Singh, William Murad
2013 BMC Bioinformatics  
Results: In this paper, we propose a novel and theoretically rigorous framework for disulfide bond determination based on information fusion from different methods using an extended formulation of Dempster-Shafer  ...  Disulfide bonds constitute one of the most important cross-linkages in proteins and significantly influence protein structure and function.  ...  Acknowledgements The authors thank Ten-Yang Yen and Bruce Macher for the glycosyltransferase tandem mass spectra.  ... 
doi:10.1186/1471-2105-14-s2-s20 pmid:23368815 pmcid:PMC3549834 fatcat:zlb5xwj52rfwzj5rn7ovzf3dwq

Implementing belief function computations

Rolf Haenni, Norbert Lehmann
2003 International Journal of Intelligent Systems  
There are two ways of making Dempster-Shafer theory more efficient.  ...  Multi-Variate Dempster-Shafer Theory The primitive elements of Dempster-Shafer theory are belief functions bel ϕ relative to some given evidence ϕ [6, 22, 1, 29, 30, 18, 17] .  ...  One of the most promising alternatives is the theory of belief functions, also known as Dempster -Shafer theory or theory of evidence.  ... 
doi:10.1002/int.10073 fatcat:b57rqn4aprgsjjya5srzl5wyzq
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