Robust combination rules for evidence theory

Florea, Mihai; Jousselme, Anne-Laure; Bossé, íloi; Grenier, Dominic

doi:10.1016/j.inffus.2008.08.007

Cited by 129 publications

(62 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, many discounting frameworks (e.g. [58,59]), mainly based on two di↵erent approaches, have been proposed to enhance the trustworthiness of information from unreliable sources of evidence. On the one hand, there are methods that propose distance metrics 8 to capture the level (viz the quantity) of the dissimilarity between di↵erent opinions, but cannot show whether or not they conflict in the hypothesis they strongly support.…”

Section: Appendix B Reliability and Discountingmentioning

confidence: 99%

Quantifying leakage in the presence of unreliable sources of information

Hamadou

Palamidessi

Sassone

2017

Journal of Computer and System Sciences

View full text Add to dashboard Cite

Belief and min-entropy leakage are two well-known approaches to quantify information flow in security systems. Both concepts stand as alternatives to the traditional approaches founded on Shannon entropy and mutual information, which were shown to provide inadequate security guarantees. In this paper we unify the two concepts in one model so as to cope with the frequent (potentially inaccurate, misleading or outdated) attackers' side information about individuals on social networks, online forums, blogs and other forms of online communication and information sharing. To this end we propose a new metric based on min-entropy that takes into account the adversary's beliefs.

show abstract

Section: Appendix B Reliability and Discountingmentioning

confidence: 99%

Quantifying leakage in the presence of unreliable sources of information

Hamadou

Palamidessi

Sassone

2017

Journal of Computer and System Sciences

View full text Add to dashboard Cite

show abstract

“…In other words, m(A) represents how strongly A is supported by evidence. The mass function needs to satisfy the following conditions (Florea, Jousselme, Bossé, & Grenier, 2009;Gordon & Shortliffe, 2008, pp.311-344;Wang et al, 2006b):…”

Section: The Er Approachmentioning

confidence: 99%

“…To use Dempster's rule, the following three conditions must be satisfied (Dempster, 1967;Dezert, 2002;Florea et al, 2009;Liu & Yager, 2008, pp.1-34;Shafer, 1990;Yang, Dale, & Siow, 2001): ① All pieces of evidence or sources of information are independent of each other; ② all propositions within a finite frame of discernment (H) must be mutually exclusive and collectively exhaustive; ③ propositions defined for all pieces of evidence must be the members of the same frame of discernment (H).…”

Section: The Er Approachmentioning

confidence: 99%

Evaluation of Corporate Sustainability

Sureeyatanapas¹,

Yang²,

Bamford³

2014

Front. Eng

View full text Add to dashboard Cite

As a consequence of an increasing demand in sustainable development for business organizations, the evaluation of corporate sustainability has become a topic intensively focused by academic researchers and business practitioners. Several techniques in the context of multiple criteria decision analysis (MCDA) have been suggested to facilitate the evaluation and the analysis of sustainability performance. However, due to the complexity of evaluation, such as a compilation of quantitative and qualitative measures, interrelationships among various sustainability criteria, the assessor's hesitation in scoring, or incomplete information, simple techniques may not be able to generate reliable results which can reflect the overall sustainability performance of a company. This paper proposes a series of mathematical formulations based upon the evidential reasoning (ER) approach which can be used to aggregate results from qualitative judgments with quantitative measurements under various types of complex and uncertain situations. The evaluation of corporate sustainability through the ER model is demonstrated using actual data generated from three sugar manufacturing companies in Thailand. The proposed model facilitates managers in analysing the performance and identifying improvement plans and goals. It also simplifies decision making related to sustainable development initiatives. The model can be generalized to a wider area of performance assessment, as well as to any cases of multiple criteria analysis.

show abstract

“…When there is little or no consistency among the evidence from different sources, the mixing or averaging rule (Sentz and Ferson, 2002) and the improved combination rule (Fan and Zuo, 2006a; are Science Publications AJEAS available. Recent studies on combination rules fall into two categories: the methods modifying Dempster rule and the methods correcting original evidence sources (Florea et al, 2009). Hegarat-Mascle et al (1997); Agarwal et al (2004) and Nikolaidis and Haftka (2001):…”

Section: Dempster's Rule Of Combinationmentioning

confidence: 99%

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Huang

Liu

et al. 2013

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

Engineering design under uncertainty has gained considerable attention in recent years. A great multitude of new design optimization methodologies and reliability analysis approaches are put forth with the aim of accommodating various uncertainties. Uncertainties in practical engineering applications are commonly classified into two categories, i.e., aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty arises because of unpredictable variation in the performance and processes of systems, it is irreducible even adding more data or knowledge. On the other hand, epistemic uncertainty stems from lack of knowledge of the system due to limited data, measurement limitations, or simplified approximations in modeling system behavior and it can be reduced by obtaining more data or knowledge. More specifically, aleatory uncertainty is naturally represented by a statistical distribution and its associated parameters can be characterized by sufficient data. If, however, the data is limited and can be quantified in a statistical sense, epistemic uncertainty can be considered as an alternative tool in such a situation. Of the several optional treatments for epistemic uncertainty, possibility theory and evidence theory have proved to be the most computationally efficient and stable for reliability analysis and engineering design optimization. This study first attempts to provide a better understanding of uncertainty in engineering design by giving a comprehensive overview of its classifications, theories and design considerations. Then a review is conducted of general topics such as the foundations and applications of possibility theory and evidence theory. This overview includes the most recent results from theoretical research, computational developments and performance improvement of possibility theory and evidence theory with an emphasis on revealing the capability and characteristics of quantifying uncertainty from different perspectives. Possibility and evidence theory-based reliability methods have many advantages for practical engineering when compared with traditional probability-based reliability methods. They can work well under limited data while the latter need large amounts of information, more than possible in engineering practice due to aleatory and epistemic uncertainties. The possible directions for future work are summarized.

show abstract

Robust combination rules for evidence theory

Cited by 129 publications

References 24 publications

Quantifying leakage in the presence of unreliable sources of information

Quantifying leakage in the presence of unreliable sources of information

Evaluation of Corporate Sustainability

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Contact Info

Product

Resources

About