A generalized Rényi divergence for multi-source information fusion with its application in EEG data analysis

Zhu, Chaosheng; Xiao, Fuyuan; Cao, Zehong

doi:10.1016/j.ins.2022.05.012

Cited by 53 publications

(19 citation statements)

References 44 publications

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“…In previous studies, the Deng entropy has shown both practicality and feasibility in fusing multi-source information [12]. Therefore, next, the definition of the Enhanced Pignistic Deng Entropy (EPDeng Entropy) is offered, which is the first belief entropy for an objective uncertainty measure on EBetP functions.…”

Section: B the Enhanced Pignistic Hellinger Distance Enhanced Pignist...mentioning

confidence: 99%

“…Step 2-2 Since the feasibility of Deng entropy in information fusion has been verified [12], use the EPDeng entropy to measure the uncertainty of EBetP s through Eq. (15):…”

Section: B the Enhanced Pignistic Hellinger Distance Enhanced Pignist...mentioning

confidence: 99%

“…al. also devised evidential fusion algorithms using Belief Hellinger distance and Generalized Belief Renyi divergence [6], [12]. However, the effects of Xiao's and Zhu's methods on decision accuracy are still in need of improvement.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

Zhang

Song

et al. 2023

IEEE Access

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The traditional Pignistic transformation is limited in the context of ''betting'', which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm's advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.

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Section: B the Enhanced Pignistic Hellinger Distance Enhanced Pignist...mentioning

confidence: 99%

“…Step 2-2 Since the feasibility of Deng entropy in information fusion has been verified [12], use the EPDeng entropy to measure the uncertainty of EBetP s through Eq. (15):…”

Section: B the Enhanced Pignistic Hellinger Distance Enhanced Pignist...mentioning

confidence: 99%

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An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

Zhang

Song

et al. 2023

IEEE Access

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“…Therefore, various theories have been established to handle uncertainty, including Z number, 3 rough sets, 4,5 random permutation set, 6–8 intuitionistic fuzzy set, 9–11 and so on 12,13 . Moreover, these theories have been applied in various fields, including data fusion, 14,15 decision making, 16–19 casual inference 20 community detection, 21,22 risk assessment, 23,24 reliability analysis, 25,26 social network analysis, 27 classification, 28,29 fault diagnosis, 30 medical diagnosis, 31,32 and so on.…”

Section: Introductionmentioning

confidence: 99%

“…including data fusion, 14,15 decision making, [16][17][18][19] casual inference 20 community detection, 21,22 risk assessment, 23,24 reliability analysis, 25,26 social network analysis, 27 classification, 28,29 fault diagnosis, 30 medical diagnosis, 31,32 and so on.…”

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confidence: 99%

Complex interval number‐based uncertainty modeling method with its application in decision fusion

Zheng

Xiao

2022

Int J of Intelligent Sys

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Complex evidence theory, a generalization of Dempster–Shafer evidence theory, is an effective uncertainty reasoning for decision fusion in complex‐valued domain. In particular, the generation of complex basic belief assignment (CBBA) is a key issue for uncertainty modeling in complex evidence theory. In this paper, we first construct complex interval number (CIN) model. In this context, we propose a novel CBBA generation method to model uncertainty in the framework of complex planes. Furthermore, we propose a novel decision‐making algorithm on the basis of the CIN‐based CBBA generation method. Through an application in pattern recognition on several real‐world data sets, the efficiency of the proposed decision‐making algorithm is verified.

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Belief $$\chi ^2$$ Divergence-Based Dynamical Complexity Analysis for Biological Systems

Zhang¹,

Xiao²

2023

Machine Learning for Cyber Security

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A generalized Rényi divergence for multi-source information fusion with its application in EEG data analysis

Cited by 53 publications

References 44 publications

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

Complex interval number‐based uncertainty modeling method with its application in decision fusion

Belief $$\chi ^2$$ Divergence-Based Dynamical Complexity Analysis for Biological Systems

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