The Experts below are selected from a list of 17493 Experts worldwide ranked by ideXlab platform
Yong Deng - One of the best experts on this subject based on the ideXlab platform.
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a generalized expression for information quality of basic Probability Assignment
IEEE Access, 2019Co-Authors: Xiaozhuan Gao, Yong DengAbstract:The information quality is widely used in many applications. However, the existing information quality can only deal with the Probability distribution. Compared with Probability distribution, the basic Probability Assignment (BPA) in evidence theory is more efficient to handle uncertainty. As a result, it is necessary to generalize the existing information quality. In this paper, a new expression for information quality is proposed to measure the information quality of BPA. When the BPA degenerates into a Probability distribution, the proposed generalized expression for information quality in this paper is degenerated into the information quality proposed by Yager. Numerical examples are used to demonstrate the effectiveness of the generalized expression for information quality. In addition, a weighted average combination rule based on the new expression for information quality is presented. A numerical example in target recognition is illustrated to show its validity in combining conflicting evidence.
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a new method to determine generalized basic Probability Assignment in the open world
IEEE Access, 2019Co-Authors: Renliang Sun, Yong DengAbstract:Dempster-Shafer evidence theory (D-S theory) is a very useful tool to solve problems in the field of information fusion. But how to determine generalized basic Probability Assignment (GBPA) more accurately and efficiently in D-S theory is still a matter of debate, especially in the open world. In this paper, we put forward a new method to determine GBPA in the open world. First, a minimum spanning tree (MST) is established for samples in each known class. The covering radius of every edge in MST of each class will be generated based on the formula for generating improved radius. The MST coverage of every class will be established. Combine these coverages to form the MST covering model. Each new sample should be justified the MST coverages it belongs to. Then, we put forward a formula to generate GBPA of the sample. Finally, determine whether the sum of GBPA is smaller than 1. If so, m(O) needs to be generated. Otherwise, GBPA needs to be standardized. The experimental results on Iris dataset prove the effectiveness of our method.
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the negation of basic Probability Assignment
IEEE Access, 2019Co-Authors: Xiaozhuan Gao, Yong DengAbstract:In many cases, we obtain information using various methods in order to make better decisions. The everything in nature and society has its negative, the negation of negation has significant meaning. Considering the problem from two aspects, we can get more accurate information. However, in most cases, the information of negation is ignored. Hence, the negation provides a new view to obtain information. However, existing negation method mainly apply to Probability distribution. How to get the negation of basic Probability Assignment (BPA) in Dempster-Shafer (D-S) theory is still an open issue. The paper proposed the new negation method of BPA. Besides, some numerical examples are given to this approach for better understanding. Moreover, in order to demonstrate the efficiency of the proposed method, the paper compared the changes of uncertainty between original and negation by using some uncertain measurement methods. Finally, practical application is used to be discussed the application of proposed method.
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the negation of a basic Probability Assignment
IEEE Transactions on Fuzzy Systems, 2019Co-Authors: Likang Yin, Xinyang Deng, Yong DengAbstract:In the field of information science, how to represent the uncertain information is still an open issue. The negation is an important way to represent the information. However, existing negation method has the limitations since it can only be applied to the Probability distributions. To address this issue, this paper proposed a novel method to obtain the negation of the basic Probability Assignment (BPA). Moreover, several methods are used to measure the uncertainty of the BPA after each negation process, and the connection between uncertain information and entropy is discussed in this paper. Furthermore, based on the negation, this paper proposed a method to measure the uncertainty of the BPA. Finally, numerical examples are used to demonstrate the efficiency of the proposed method.
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a new belief entropy to measure uncertainty of basic Probability Assignments based on belief function and plausibility function
Entropy, 2018Co-Authors: Lipeng Pan, Yong DengAbstract:How to measure the uncertainty of the basic Probability Assignment (BPA) function is an open issue in Dempster-Shafer (D-S) theory. The main work of this paper is to propose a new belief entropy, which is mainly used to measure the uncertainty of BPA. The proposed belief entropy is based on Deng entropy and Probability interval consisting of lower and upper probabilities. In addition, under certain conditions, it can be transformed into Shannon entropy. Numerical examples are used to illustrate the efficiency of the new belief entropy in measurement uncertainty.
Taeseung Choi - One of the best experts on this subject based on the ideXlab platform.
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quantum Probability Assignment limited by relativistic causality
Scientific Reports, 2016Co-Authors: Yeong Deok Han, Taeseung ChoiAbstract:Quantum theory has nonlocal correlations, which bothered Einstein, but found to satisfy relativistic causality. Correlation for a shared quantum state manifests itself, in the standard quantum framework, by joint Probability distributions that can be obtained by applying state reduction and Probability Assignment that is called Born rule. Quantum correlations, which show nonlocality when the shared state has an entanglement, can be changed if we apply different Probability Assignment rule. As a result, the amount of nonlocality in quantum correlation will be changed. The issue is whether the change of the rule of quantum Probability Assignment breaks relativistic causality. We have shown that Born rule on quantum measurement is derived by requiring relativistic causality condition. This shows how the relativistic causality limits the upper bound of quantum nonlocality through quantum Probability Assignment.
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Quantum Probability Assignment limited by relativistic causality
arXiv: Quantum Physics, 2013Co-Authors: Taeseung ChoiAbstract:The quantum nonlocality is limited by relativistic causality, however, the reason is not fully understood yet. The relativistic causality condition on nonlocal correlations has been usually accepted as a prohibition of faster-than-light signaling, called no-signaling condition. We propose another causality condition from the observation that space-like separate events should have no causal relationship. It is proved that the new condition is stronger than no-signaling condition for a pair of binary devices. We derive the standard Probability Assignment rule, so-called Born rule, on quantum measurement, which determines the degree of quantum nonlocality, by using relativistic causality constraint. This shows how the causality limits the upper bound of quantum nonlocality through quantum Probability Assignment.
Yongchuan Tang - One of the best experts on this subject based on the ideXlab platform.
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conflict data fusion in a multi agent system premised on the base basic Probability Assignment and evidence distance
Entropy, 2021Co-Authors: Jingyu Liu, Yongchuan TangAbstract:The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic Probability Assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.
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a new approach for generation of generalized basic Probability Assignment in the evidence theory
Pattern Analysis and Applications, 2021Co-Authors: Yongchuan Tang, Zijing LiuAbstract:The process of information fusion needs to deal with a large number of uncertain information with multi-source, heterogeneity, inaccuracy, unreliability, and incompleteness. In practical engineering applications, Dempster–Shafer evidence theory is widely used in multi-source information fusion owing to its effectiveness in data fusion. Information sources have an important impact on multi-source information fusion in an environment with the characteristics of complex, unstable, uncertain, and incomplete. To address multi-source information fusion problem, this paper considers the situation of uncertain information modeling from the closed-world to the open-world assumption and studies the generation of basic Probability Assignment with incomplete information. A new method is proposed to generate the generalized basic Probability Assignment (GBPA) based on the triangular fuzzy number model under the open-world assumption. First, the maximum, minimum, and mean values for the triangular membership function of each attribute in classification problem can be obtained to construct a triangular fuzzy number representation model. Then, by calculating the length of the intersection points between the sample and the triangular fuzzy number model, a GBPA set with an Assignment for the empty set can be determined. The proposed method can not only be used in different complex environments simply and flexibly, but also have less information loss in information processing. Finally, a series of comprehensive experiments basing on the UCI data sets is used to verify the rationality and superiority of the proposed method.
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a new base basic Probability Assignment approach for conflict data fusion in the evidence theory
Applied Intelligence, 2021Co-Authors: Ming Jing, Yongchuan TangAbstract:Dempster-Shafer evidence theory (D-S theory) is applied to process uncertain information in different scenarios. However, traditional Dempster combination rule may produce counterintuitive results while dealing with highly conflicting data. Inspired by a perspective of constructing base belief function for conflicting data processing in D-S theory, a new base basic Probability Assignment (bBPA) method is proposed to process the potential conflict before data fusion. Instead of assigning initial belief on the whole power set space, the new method assigns the base belief to basic events in the frame of discernment. Consequently, the bBPA is consistent with the classical Probability theory. Several numerical examples are adopted to verify the reliability and accuracy of the method in processing highly conflicting data. The data sets in the University of California Irvine (UCI) Machine Learning Repository are used to verity the availability of the new method in classification problem. Experimental result shows that the new method has some superiority in dealing with highly conflicting data.
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an improved data fusion method based on weighted belief entropy considering the negation of basic Probability Assignment
Journal of Mathematics, 2020Co-Authors: Yong Chen, Yongchuan Tang, Yan LeiAbstract:Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic Probability Assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.
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a new classification method based on the negation of a basic Probability Assignment in the evidence theory
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Zijing Liu, Yongchuan TangAbstract:Abstract In the practical application of classification, how to handle uncertain information for efficient classification is a hot topic. In this paper, in the frame of Dempster–Shafer evidence theory, a new classification method based on the negation of basic Probability Assignment (BPA) is proposed to implement an effective classification. The proposed method addresses the issue that the values of samples’ attributes cannot clearly point out a certain class in classification problems. For uncertain information modeling, the negation of BPA is adopted to obtain more valuable information in the body of evidence. To measure the uncertain information represented by the negation of BPA, the belief entropy is used for calculating the uncertain degree of each body of evidence. Finally, Dempster’s combination rule is used for data fusion to identify and recognize the unknown class. The effectiveness and efficiency of the new classification method are validated according to experiments on several UCI data sets. In addition, the classification experiment on the data sets with the changing proportion of the training set verifies that the method is robust and feasible.
Fuyuan Xiao - One of the best experts on this subject based on the ideXlab platform.
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a new base function in basic Probability Assignment for conflict management
Applied Intelligence, 2021Co-Authors: Fuyuan XiaoAbstract:To address highly conflicting evidence combinations, a new base function is proposed to alleviate conflicts that exist in pieces of evidence provided before the fusion of them to get intuitive results from the combination. The proposed method assigns a corresponding value to each proposition according to its importance. Single subset propositions are considered more crucial than multiple ones, which intends to reduce uncertainties existing in the frame of discernment so that indicative results of combination can be obtained. More than that, to avoid a considerable deviation from the modified mass to the original ones, an operation of average is carried out twice to achieve this effect. The proposed conflicting management method not only has the advantage of eliminating conflicts among evidence but also the ability to produce intuitive results. Several numerical examples and experiments using datasets are illustrated to verify the accuracy and correctness of the proposed method in processing highly conflicting information.
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an improved approach to generate generalized basic Probability Assignment based on fuzzy sets in the open world and its application in multi source information fusion
Applied Intelligence, 2020Co-Authors: Yi Fan, Fuyuan XiaoAbstract:The generalized evidence theory (GET) is an efficient mathematical methodology to deal with multi-source information fusion problems. The GET has the capability of handling uncertain problems even in the open world. In real world applications, some noise or other disturbance often makes the multi-source information have uncertainty. Thus, how to reliably generate the generalized basic Probability Assignment (GBPA) is a key problem of GET, especially under the noisy environment. Therefore, in this paper, we propose a novel approach to generate GBPA with high robustness by using a cluster method. In this way, the proposed model has the ability to correctly identify the target even under a noisy environment. In particular, the k-means++ algorithm based on triangular fuzzy number is applied to build the GBPA generation model. According to the proposed GBPA generation model, the related similarity degree is calculated for each test instance. After resolving the existing conflicts, the final GBPAs are obtained by using the generalized combination rule. To demonstrate the effectiveness of the proposed method, we compare the proposed approach with related work in the applications of classification and fault diagnosis problems, respectively. Through experimental analysis, it is verified that the proposed approach has the best robustness to generate the GBPAs and maintain a high recognition rate under both noisy and noiseless environments.
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an improved method to determine basic Probability Assignment with interval number and its application in classification
International Journal of Distributed Sensor Networks, 2019Co-Authors: Bowen Qin, Fuyuan XiaoAbstract:Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probabi...
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Negation of Basic Probability Assignment: Trends of Dissimilarity and Dispersion
IEEE Access, 2019Co-Authors: Fuyuan XiaoAbstract:In the field of knowledge representation, negation has been introduced so that practical issues can be modelled more effectively. The negation of Probability was first formally determined by Zadeh, with its basic properties proposed by Yager. Recent studies have extended the negation of Probability to that of basic Probability Assignment (BPA) by introducing Dempster-Shafer theory which is believed to perform well in dealing with uncertainty problems. Besides, the negation model has been proved to have the maximum entropy allocation, which attracts studies on uncertainty measures that can be applied in the negation process. In this paper, we have mainly investigated the trend of dissimilarity between two BPAs in the negation process. In particular, an evidence distance proposed by Jousselme et al. is used to serve as a dissimilarity measure to help quantify the variation trends. Moreover, standard deviation is used in this study to represent the dispersion in a BPA. Through our analysis, we obtained some interesting properties finally with their generalizations discussed in a proposed framework of negation methods.
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A Non-Parametric Method to Determine Basic Probability Assignment Based on Kernel Density Estimation
IEEE Access, 2018Co-Authors: Fuyuan XiaoAbstract:Dempster–Shafer evidence theory has been extensively applied in a variety of fields due to its ability to solve knowledge reasoning and decision-making problem under uncertain environments. Nevertheless, it is still an open issue about how to determine the basic Probability Assignment (BPA). In this paper, a new non-parametric method based on kernel density estimation is proposed to determine BPA. First, the Probability density function of each attribute is calculated, which can be regarded as the Probability model for the related attribute using the training sample. Then, a nested BPA function is constructed using the intersections point of test sample and Probability models. Finally, Dempster’s combination rule is used to combine multiple BPAs to get the final BPA. Some classification experiments are conducted on several datasets. The experimental results demonstrate that the proposed method is more effective and reasonable in determining BPAs, which has a better classification performance than the existing method.
Sohel Anwar - One of the best experts on this subject based on the ideXlab platform.
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time domain data fusion using weighted evidence and dempster shafer combination rule application in object classification
Sensors, 2019Co-Authors: Nazmuzzaman Khan, Sohel AnwarAbstract:To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic Probability Assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps( t s ).
