Logical method for logical operations based on evidential reasoning
International Journal of Knowledge Engineering and Soft Data Paradigms
Minimum cost consensus with quadratic cost functions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
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IEEE Transactions on Intelligent Transportation Systems
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Expert Systems with Applications: An International Journal
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Assessment of E-Commerce security using AHP and evidential reasoning
Expert Systems with Applications: An International Journal
Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
A new method to determine basic probability assignment from training data
Knowledge-Based Systems
Generic discounting evaluation approach for urban image classification
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
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Expert Systems with Applications: An International Journal
Information-based dissimilarity assessment in Dempster-Shafer theory
Knowledge-Based Systems
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This paper presents a new framework for sensor reliability evaluation in classification problems based on evidence theory (or the Dempster-Shafer theory of belief functions). The evaluation is treated as a two-stage training process. First, the authors assess the static reliability from a training set by comparing the sensor classification readings with the actual values of data, which are both represented by belief functions. Information content contained in the actual values of each target is extracted to determine its influence on the evaluation. Next, considering the ability of the sensor to understand a dynamic working environment, the dynamic reliability is evaluated by measuring the degree of consensus among a group of sensors. Finally, the authors discuss why and how to combine these two kinds of reliabilities. A significant improvement using the authors' method is observed in numerical simulations as compared with the recently proposed method