A Variation of the Algorithm to Achieve the Maximum Entropy for Belief Functions [PDF]
Entropy, 2023 Evidence theory (TE), based on imprecise probabilities, is often more appropriate than the classical theory of probability (PT) to apply in situations with inaccurate or incomplete information.
Joaquín Abellán +2 more
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A New Correlation Measure for Belief Functions and Their Application in Data Fusion [PDF]
Entropy, 2023 Measuring the correlation between belief functions is an important issue in Dempster–Shafer theory. From the perspective of uncertainty, analyzing the correlation may provide a more comprehensive reference for uncertain information processing.
Zhuo Zhang +3 more
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Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions. [PDF]
IEEE Trans Image Process, 2019 Precise delineation of target tumor is a key factor to ensure the effectiveness of radiation therapy. While hybrid positron emission tomography-computed tomography (PET-CT) has become a standard imaging tool in the practice of radiation oncology, many ...
Lian C, Ruan S, Denoeux T, Li H, Vera P.
europepmc +2 more sources
Genetic Algorithm Based on a New Similarity for Probabilistic Transformation of Belief Functions [PDF]
Entropy, 2022 Recent studies of alternative probabilistic transformation (PT) in Dempster–Shafer (DS) theory have mainly focused on investigating various schemes for assigning the mass of compound focal elements to each singleton in order to obtain a Bayesian belief ...
Yilin Dong, Lei Cao, Kezhu Zuo
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Sensor-Model Matching for Controlled Comparison of Bayesian and Belief-Function Occupancy Grid Fusion [PDF]
SensorsComparisons of Bayesian log-odds and Dempster’s combination rule for occupancy grid mapping typically parameterize the two sensor models independently, so that observed performance differences confound the fusion rule with the sensor parameterization. We
Tatiana Berlenko, Kirill Krinkin
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Belief Interval-Based Distance Measures in the Theory of Belief Functions
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018 In belief functions related fields, the distance measure is an important concept, which represents the degree of dissimilarity between bodies of evidence. Various distance measures of evidence have been proposed and widely used in diverse belief function
Deqiang Han, J. Dezert, Yi Yang
semanticscholar +3 more sources
Application of belief functions to medical image segmentation: A review [PDF]
Information Fusion, 2022 The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertainty analysis and multiple evidence fusion, has made significant ...
Ling Huang, S. Ruan, Thierry Denoeux
semanticscholar +1 more source
BF-QC: Belief functions on quantum circuits [PDF]
Expert systems with applications, 2021 Dempster-Shafer Theory (DST) of belief function is a basic theory of artificial intelligence, which can represent the underlying knowledge more reasonably than Probability Theory (ProbT). Because of the computation complexity exploding exponentially with
Qianli Zhou, Guojing Tian, Yong Deng
semanticscholar +1 more source
The application of Dempster-Shafer theory demonstrated with justification provided by legal evidence [PDF]
Judgment and Decision Making, 2007 In forecasting and decision making, people can and often do represent a degree of belief in some proposition. At least two separate constructs capture such degrees of belief: likelihoods capturing evidential balance and support capturing evidential ...
Shawn P. Curley
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Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS
Applied Sciences, 2021 This work is related to the diagnosis process in intelligent tutoring systems (ITS). This process is usually a complex task that relies on imperfect data. Indeed, learning data may suffer from imprecision, uncertainty, and sometimes contradictions.
Nesrine Rahmouni +3 more
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