Combination of Conflicting Evidence Based on Relative Entropy

2015 ◽  
Vol 724 ◽  
pp. 318-322
Author(s):  
Shang Wu ◽  
Guo Chu Chen
Keyword(s):  
Relative Entropy ◽  
Evidence Theory ◽  
Final Outcome ◽  
Evidence Based ◽  
Conflicting Evidence ◽  
Extract Information

Combination of conflicting evidence usually resulted in illogical outcome. In order to solve this problem, an approach to acquire evidence reliability based on relative entropy was proposed by researching the evidence theory and relative entropy. Firstly, the weights of evidences were calculated by evidence distance, reference evidence was obtained by weighting and meaning. Then relative reliabilities were achieved by calculating the relative entropy between original evidence and reference evidence. In the end, final outcome was achieved by D-S composition. This method can commendably extract information in the conflict evidences. The result of the simulation proves it well.

scholarly journals Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 762
Author(s):  
Shuai Yuan ◽  
Honglei Wang
Keyword(s):  
Decision Rule ◽  
Historical Data ◽  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Final Decision ◽  
Evidence Based ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
The Difference

In a multi-sensor system, due to the difference of performance of sensors and the environment in which the sensor collects evidence, evidence collected will be highly conflicting, which leads to the failure of D-S evidence theory. The current research on combination methods of conflicting evidence focuses on eliminating the problem of "Zadeh paradox" brought by conflicting evidence, but do not distinguish the evidence from different sources effectively. In this paper, the credibility of each piece of evidence to be combined is weighted based on historical data, and the modified evidence is obtained by weighted average. Then the final result is obtained by combining the modified evidence using D-S evidence theory, and the improved decision rule is used for the final decision. After the decision, the system updates and stores the historical data based on actual results. The improved decision rule can solve the problem that the system cannot make a decision when there are two or more propositions corresponding to the maximum support in the final combination result. This method satisfies commutative law and associative law, so it has the symmetry that can meet the needs of the combination of time-domain evidence. Numerical examples show that the combination method of conflict evidence based on historical data can not only solve the problem of “Zadeh paradox”, but also obtain more reasonable results.

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

scholarly journals Evidence-based recommender system and experimental validation for high-entropy alloys

2021 ◽  
Author(s):  
Minh-Quyet Ha ◽  
Nguyen-Duong Nguyen ◽  
Viet-Cuong Nguyen ◽  
Takahiro Nagata ◽  
Toyohiro Chikyow ◽  
...  
Keyword(s):  
Recommender System ◽  
Evidence Theory ◽  
Ternary Alloys ◽  
Evidence Based ◽  
Data Sets ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Data Driven Approach ◽  
Numerous Element ◽  
Fold Cross Validation

Abstract We present a data-driven approach to explore high-entropy alloys (HEAs). To overcome the challenges with numerous element-combination candidates, selecting appropriate descriptors, and the limitations and biased of existing data, we apply the evidence theory to develop a descriptor-free evidence-based recommender system (ERS) for recommending HEAs. The proposed system measures the similarities between element combinations and utilizes it to recommend potential HEAs. To evaluate the ERS, we compare its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known data sets, including binary and ternary alloys. The results of experiments using k-fold cross-validation on the data sets show that the ERS outperforms all competitors. Furthermore, the ERS shows excellent extrapolation capabilities in experiments of recommending quaternary and quinary HEAs. We experimentally validate the most strongly recommended Fe-Co-based magnetic HEA, viz. FeCoMnNi, and confirm that it shows a body-centered cubic structure and is stable at high temperatures.

scholarly journals Synergetic Learning Model: The Sum is Greater

2020 ◽  
Vol 13 (1) ◽  
pp. 62
Author(s):  
Kevin S. Thompson
Keyword(s):  
Instructional Design ◽  
Human Resource Development ◽  
Learning Model ◽  
Learning Objectives ◽  
Evidence Based ◽  
Improve Performance ◽  
Design Models ◽  
Design Learning ◽  
And Performance ◽  
Extract Information

<p>Instructional design models provide process insight that guides learning solution designers in their work to meet learning objectives and improve performance. Human resource development scholars often focus on individual learning components that support meeting learning and performance objectives. Learning practitioners who design learning solutions must review significant amounts of research studies to extract information that may assist them in their roles. This paper introduces the evidence-based Synergetic Learning Model, focused exclusively on design, intended to leverage the work of scholars holistically for the benefit of learning practitioners.</p>

scholarly journals Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 890
Author(s):  
Sergey Oladyshkin ◽  
Farid Mohammadi ◽  
Ilja Kroeker ◽  
Wolfgang Nowak
Keyword(s):  
Bayesian Inference ◽  
Active Learning ◽  
Gaussian Process ◽  
Relative Entropy ◽  
Bayesian Model ◽  
Information Gain ◽  
Measurement Data ◽  
Superior Performance ◽  
Evidence Based ◽  
Model Evidence

Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.

Evidence-Based Structural Uncertainty Quantification by Dimension Reduction Decomposition and Marginal Interval Analysis

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Lixiong Cao ◽  
Jie Liu ◽  
Chao Jiang ◽  
Zhantao Wu ◽  
Zheng Zhang
Keyword(s):  
Dimension Reduction ◽  
Uncertainty Quantification ◽  
Interval Analysis ◽  
Evidence Theory ◽  
Evidence Based ◽  
Original Performance ◽  
Engineering Applications ◽  
Performance Function ◽  
One Dimensional ◽  
Interval Analysis Method

Abstract Evidence theory has the powerful feature to quantify epistemic uncertainty. However, the huge computational cost has become the main obstacle of evidence theory on engineering applications. In this paper, an efficient uncertainty quantification (UQ) method based on dimension reduction decomposition is proposed to improve the applicability of evidence theory. In evidence-based UQ, the extremum analysis is required for each joint focal element, which generally can be achieved by collocating a large number of nodes. Through dimension reduction decomposition, the response of any point can be predicted by the responses of corresponding marginal collocation nodes. Thus, a marginal collocation node method is proposed to avoid the call of original performance function at all joint collocation nodes in extremum analysis. Based on this, a marginal interval analysis method is further developed to decompose the multidimensional extremum searches for all joint focal elements into the combination of a few one-dimensional extremum searches. Because it overcomes the combinatorial explosion of computation caused by dimension, this proposed method can significantly improve the computational efficiency for evidence-based UQ, especially for the high-dimensional uncertainty problems. In each one-dimensional extremum search, as the response at each marginal collocation node is actually calculated by using the original performance function, the proposed method can provide a relatively precise result by collocating marginal nodes even for some nonlinear functions. The accuracy and efficiency of the proposed method are demonstrated by three numerical examples and two engineering applications.

scholarly journals A Self-Adaptive Combination Method in Evidence Theory Based on the Power Pignistic Probability Distance

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 526
Author(s):  
Jian Wang ◽  
Jing-wei Zhu ◽  
Yafei Song
Keyword(s):  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Multiple Sources ◽  
Adaptive Combination ◽  
Conflicting Evidence ◽  
Different Types ◽  
Fusion Methods ◽  
Temporal And Spatial ◽  
Probability Distance

Existing methods employed for combining temporal and spatial evidence derived from multiple sources into a single coherent description of objects and their environments lack versatility in various applications such as multi-sensor target recognition. This is addressed in the present study by proposing an adaptive evidence fusion method based on the power pignistic probability distance. This method classifies evidence sets into non-conflicting and conflicting evidence sets based on the maximum power pignistic probability distance obtained between evidence pairs in the evidence set. Non-conflicting evidence sets are fused using Dempster’s rule, while conflicting evidence sets are fused using a weighted average combination method based on the power pignistic probability distance. The superior evidence fusion performance of the proposed method is demonstrated by comparisons with the performances of seven other fusion methods based on numerical examples with four different evidence conflict scenarios. The results show that the method proposed in this paper not only can properly fuse different types of evidence, but also provides an excellent focus on the components of evidence sets with high confidence, which is conducive to timely and accurate decisions.

scholarly journals Research on the Combination Method of Temporal Conflict Evidence Based on Transition Matrix

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bin Wu ◽  
Xiao Yi
Keyword(s):  
Transition Matrix ◽  
Transition Probability ◽  
Evidence Theory ◽  
Research Topic ◽  
Combination Method ◽  
Evidence Based ◽  
Important Research ◽  
Transition Matrices ◽  
Important Research Topic ◽  
Evidence Combination

Conflict evidence combination is an important research topic in evidence theory. In this paper, two kinds of transition matrices are constructed based on the Markov model; one is the unordered transition matrix, which satisfies the commutative law, and the other is the temporal transition matrix, which does not satisfy the commutative law, but it can handle the combination of temporal evidence well. Then, a temporal conflict evidence combination model is proposed based on these two transition matrices. First, the transition probability at the first n time is calculated through the model of unordered transition probability, and then, the transition matrix from the N + 1 time is used to solve the combination problem of temporal conflict evidence. The effectiveness of the transition matrix in the research of conflict evidence combination method is proved by the example analysis.

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