scholarly journals Mass Collaboration-Driven Method for Recommending Product Ideas Based on Dempster-Shafer Theory of Evidence

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yuan-Wei Du ◽  
Yu-Kun Shan ◽  
Chang-Xing Li ◽  
Rui Wang
Keyword(s):  
Comprehensive Evaluation ◽  
Value Orientations ◽  
Combination Rule ◽  
Mass Collaboration ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Evaluation Information ◽  
Collaboration Mode

In the mass collaboration mode, there exist a large number of product ideas with low value density and thousands of participants who are differed on their professional backgrounds, knowledge structures, and value orientations. It is impossible for each participant to give a comprehensive evaluation of each idea as that in traditional methods for the reasons as mentioned above. In order to solve this problem, a mass collaboration-driven method for recommending product ideas is proposed based on Dempster-Shafer theory of evidence (DST). Firstly, the method for computing basic probability assignment (BPA) function, which can effectively reflect the facticity of experts’ evaluations, is introduced by discounting belief degrees with weights to extract the evaluation information of product ideas. Then, Dempster’s combination rule is used to combine the derived BPA functions for two times: the first one is to combine the discounted BPA functions on all criteria with respect to a specified expert and the other is to combine the combined BPA functions for all experts with respect to a specified alternative. Finally, the steps of mass collaboration-driven method for recommending product ideas based on the DST are proposed. An illustrative example is provided to demonstrate the applicability of the proposed method.

scholarly journals Analysis of “mixing” combination rules and Smet’s combination rule

2019 ◽  
pp. 4-8
Author(s):  
Sofiia Alpert
Keyword(s):  
Satellite Images ◽  
Probability Distributions ◽  
Evidence Theory ◽  
Combination Rule ◽  
Multiple Sources ◽  
Combination Rules ◽  
Spectral Bands ◽  
Basic Probability ◽  
Shafer Theory ◽  
Theory Of Evidence

The process of solution of different practical and ecological problems, using hyperspectral satellite images usually includes a procedure of classification. Classification is one of the most difficult and important procedures. Some image classification methods were considered and analyzed in this work. These methods are based on the theory of evidence. Evidence theory can simulate uncertainty and process imprecise and incomplete information. It were considered such combination rules in this paper: “mixing” combination rule (or averaging), convolutive x-averaging (or c-averaging) and Smet’s combination rule. It was shown, that these methods can process the data from multiple sources or spectral bands, that provide different assessments for the same hypotheses. It was noted, that the purpose of aggregation of information is to simplify data, whether the data is coming from multiple sources or different spectral bands. It was shown, that Smet’s rule is unnormalized version of Dempster rule, that applied in Smet’s Transferable Belief Model. It also processes imprecise and incomplete data. Smet’s combination rule entails a slightly different formulation of Dempster-Shafer theory. Mixing (or averaging) rule was considered in this paper too. It is the averaging operation that is used for probability distributions. This rule uses basic probability assignments from different sources (spectral bands) and weighs assigned according to the reliability of the sources. Convolutive x-averaging (or c-averaging) rule was considered in this paper too. This combination rule is a generalization of the average for scalar numbers. This rule is commutative and not associative. It also was noted, that convolutive x-averaging (c-averaging) rule can include any number of basic probability assignments. It were also considered examples, where these proposed combination rules were used. Mixing, convolutive x-averaging (c-averaging) rule and Smet’s combination rule can be applied for analysis of hyperspectral satellite images, in remote searching for minerals and oil, solving different environmental and thematic problems.

scholarly journals Combining Fuzzy Logic and Dempster-Shafer Theory

2015 ◽  
Vol 16 (3) ◽  
pp. 583
Author(s):  
Andino Maseleno ◽  
Md. Mahmud Hasan ◽  
Norjaidi Tuah
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Mathematical Theory ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Basic Probability Assignment ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Shafer Theory ◽  
Theory Of Evidence

This research aims to combine the mathematical theory of evidence with the rule based logics to refine the predictable output. Integrating Fuzzy Logic and Dempster-Shafer theory by calculating the similarity between Fuzzy membership function. The novelty aspect of this work is that basic probability assignment is proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get a basic probability assignment. The Dempster-Shafer mathematical theory of evidence has attracted considerable attention as a promising method of dealing with some of the basic problems arising in combination of evidence and data fusion. Dempster-Shafer theory provides the ability to deal with ignorance and missing information. The foundation of Fuzzy logic is natural language which can help to make full use of expert information.

scholarly journals A MODIFIED D NUMBERS METHODOLOGY FOR ENVIRONMENTAL IMPACT ASSESSMENT

2017 ◽  
Vol 24 (2) ◽  
pp. 653-669 ◽  
Author(s):  
Ningkui WANG ◽  
Daijun WEI
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Previous Method ◽  
Combination Process ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
D Numbers ◽  
Commutative Property

Environmental impact assessment (EIA) is usually evaluated by many factors influenced by various kinds of uncertainty or fuzziness. As a result, the key issues of EIA problem are to rep­resent and deal with the uncertain or fuzzy information. D numbers theory, as the extension of Dempster-Shafer theory of evidence, is a desirable tool that can express uncertainty and fuzziness, both complete and incomplete, quantitative or qualitative. However, some shortcomings do exist in D numbers combination process, the commutative property is not well considered when multiple D numbers are combined. Though some attempts have made to solve this problem, the previous method is not appropriate and convenience as more information about the given evaluations rep­resented by D numbers are needed. In this paper, a data-driven D numbers combination rule is proposed, commutative property is well considered in the proposed method. In the combination process, there does not require any new information except the original D numbers. An illustrative example is provided to demonstrate the effectiveness of the method.

Knowledge reduction in random information systems via Dempster–Shafer theory of evidence

2005 ◽  
Vol 174 (3-4) ◽  
pp. 143-164 ◽  
Author(s):  
Wei-Zhi Wu ◽  
Mei Zhang ◽  
Huai-Zu Li ◽  
Ju-Sheng Mi
Keyword(s):  
Information Systems ◽  
Knowledge Reduction ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence

A Framework for Decision Fusion in Image Forensics Based on Dempster–Shafer Theory of Evidence

2013 ◽  
Vol 8 (4) ◽  
pp. 593-607 ◽  
Author(s):  
Marco Fontani ◽  
Tiziano Bianchi ◽  
Alessia De Rosa ◽  
Alessandro Piva ◽  
Mauro Barni
Keyword(s):  
Image Forensics ◽  
Decision Fusion ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence
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