Combining time-series evidence: A complex network model based on a visibility graph and belief entropy

Song, Xingjian; Xiao, Fuyuan

doi:10.1007/s10489-021-02956-5

Cited by 58 publications

(17 citation statements)

References 57 publications

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“…So, the modification of evidence is taken into consideration. 46 As for preprocessing the body of evidence, scholars try to alter the initial conflicting evidence to reasonable ones. Distance, divergence measure and correlation coefficient are important factors.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Zhang

Xiao

2022

Int J of Intelligent Sys

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Dempster-Shafer (D-S) evidence theory is invaluable in the domain of multisource information fusion for handing uncertainty problems. However, there may be counter-intuitive phenomenon when facing highly conflicting information. In this paper, a novel symmetric enhanced belief χ 2 divergence measure, called SEBχ 2 , is proposed to measure the discrepancy between basic probability assignments (BPAs). The SEBχ 2 divergence consider the features of BPAs as the influence of both single-element subsets and multielement subsets is taken into account. Furthermore, the SEBχ 2 divergence is proven to be symmetric, nonnegative and nondegenerate, which are desirable properties for conflict management. Then, a new algorithm for multisource information fusion based on the SEBχ 2 divergence measure is derived. Finally, an application for pattern classification is used to illustrate the superiority of the proposed SEBχ 2 divergence measure-based fusion method over other existing well-known and recent related works with a better classification accuracy of 94.39%.

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Section: Literature Reviewmentioning

confidence: 99%

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Zhang

Xiao

2022

Int J of Intelligent Sys

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“…Physarum algorithm have show a great ability in graph theory [17], in shortest path problem [18] at the beginning and network design [10,19,20] as well as the following traffic network optimizing [19,21]. Network modelling complex system is an open issue [22][23][24][25]. Physarum algorithm also prove its adaptivity in uncertainty network [26].In this paper, Physarum algorithm was utilized to calculate the optimal flux in each path.…”

Section: Physarum Algorithmmentioning

confidence: 99%

Physarum-based Emergence Evacuation Plan

Yang

Deng

Pelusi

2022

Preprint

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The rapid urbanization in many countries, especially in some developing countries keeps enlarging the size of cities and the hight of buildings. However, the rise of population would also increase the risk of emergence situation that need large evacuation like fire and earth quake. While many researchers aims to build evacuation and evaluation model in different situations, some researchers tried to build a guide system for quick evacuation based on monitors and other high-tech devices. This paper severs to proposed a dependence-less evacuation plan for buildings and cities by flow guidance. Physarum algorithm and user equilibrium was adopted to estimate the optimal flux in each link, and triangular fuzz number was used to handle the uncertainty in emergence situation. Two numeric example was utilized to prove the efficiency of the proposed method.

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“…To solve these problems, a data-driven risk assessment model, based on Dempster-Shafer evidence theory (DST) [20,21], Deng entropy [22] and risk matrix [23], is proposed. Due to effectively deal with uncertain information, DST is widely used in decisionmaking [24][25][26], risk analysis [27], information fusion [28,29], uncertainty measurements [30], fault diagnosis [31][32][33], time-series [34], IoT applications [35] and many other fields [36,37]. Since most experts prefer to express their opinions with linguistic information, such as good, better, best, bad, worse, worst, DST can effectively deal with uncertain information about linguistic expressions involved in risk evaluation [38,39].…”

Section: Introductionmentioning

confidence: 99%

An Evidential Software Risk Evaluation Model

Deng

2022

Mathematics

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Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

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Combining time-series evidence: A complex network model based on a visibility graph and belief entropy

Cited by 58 publications

References 57 publications

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Physarum-based Emergence Evacuation Plan

An Evidential Software Risk Evaluation Model

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