Constructing confidence belief functions from one expert

Yang, Shanlin; Fu, Chao

doi:10.1016/j.eswa.2008.10.054

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…This theory can be used to model information coming from an expert, as Yang & Fu (2009) does, or from a crowd in the context of crowdsourcing, as Thierry et al (2019) does.…”

Section: Belief Functionsmentioning

confidence: 99%

Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context

Thierry¹,

Martin²,

Dubois³

et al. 2023

Preprint

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Crowdsourcing is the outsourcing of tasks to a crowd of contributors on a dedicated platform. The crowd on these platforms is very diversified and includes various profiles of contributors which generates data of uneven quality. However, majority voting, which is the aggregating method commonly used in platforms, gives equal weight to each contribution. To overcome this problem, we propose a method, MONITOR, which estimates the contributor's profile and aggregates the collected data by taking into account their possible imperfections thanks to the theory of belief functions. To do so, MONITOR starts by estimating the profile of the contributor through his qualification for the task and his behavior.Crowdsourcing campaigns have been carried out to collect the necessary data to test MONITOR on real data in order to compare it to existing approaches.The results of the experiments show that thanks to the use of the MONITOR method, we obtain a better rate of correct answer after aggregation of the contributions compared to the majority voting. Our contributions in this article are for the first time the proposal of a model that takes into account both the qualification of the contributor and his behavior in the estimation of his profile.For the second one, the weakening and the aggregation of the answers according to the estimated profiles.

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“…This theory can be used to model information coming from an expert, as Yang & Fu (2009) does, or from a crowd in the context of crowdsourcing, as Thierry et al (2019) does.…”

Section: Belief Functionsmentioning

confidence: 99%

Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context

Thierry¹,

Martin²,

Dubois³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Modeling as a function of belief has no generic solution and, generally, it depends on the application [9,10]. Based on Shafer's work [3] on simple basic belief assignments (SBBA), Smets proposed the notion of generalized simple BBA (GSBBA) [11].…”

Section: Belief Function Theory 211 Basic Conceptsmentioning

confidence: 99%

Evidential calibration process of multi-agent based system: An application to forensic entomology

Veremme

Lefèvre

Morvan³

et al. 2012

Expert Systems with Applications

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Forensic entomology consists, during a criminal investigation, in studying the insects found on a cadaver to estimate the time of death. This is the only technique that can be used for a large post-morten interval. But, because of the important system complexity, the result given by the expert are imperfect. In this paper, a Decision Support System (DSS) has been developed to take into account all the ecosystemic parameters and a signicant quantity of biological models. The proposed DSS is based on the belief function theory to validate and calibrate agent based simulations. First results of this architecture are presented within the framework of a real forensic examination.

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“…It has been applied in a wide range of areas including information fusion [7,74,82,96], expert systems [1,13,18,75,92], pattern recognition and classification [15][16][17][23][24][25]30,48,70], diagnosis and reasoning [10,21,[36][37][38]51,52,54,55,69], knowledge reduction [83][84][85], decision analysis [8,[11][12][13][14]46,74,80,86,88,90,[93][94][95], intelligent control [33], process monitoring [71][72][73], audit risk assessment where / is an empty set, A is any subset of X, and 2 X is the power set of X consisting of all the subs...…”

Section: Introductionmentioning

confidence: 99%

Maximal confidence intervals of the interval-valued belief structure and applications

Wang

et al. 2011

Information Sciences

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Constructing confidence belief functions from one expert

Cited by 13 publications

References 34 publications

Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context

Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context

Evidential calibration process of multi-agent based system: An application to forensic entomology

Maximal confidence intervals of the interval-valued belief structure and applications

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