IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v176y2023ics0960077923009888.html
   My bibliography  Save this article

Conflicting evidence fusion using a correlation coefficient-based approach in complex network

Author

Listed:
  • Tang, Yongchuan
  • Dai, Guoxun
  • Zhou, Yonghao
  • Huang, Yubo
  • Zhou, Deyun

Abstract

Dempster–Shafer evidence theory (D–S theory) can effectively deal with uncertain information and it is one of the effective data fusion methods. However, Dempster’s combination rule of D–S theory often produces counter-intuitive fusion results when the handled body of evidence (BOE) is highly conflicting with each other. Therefore, many new methods have been gradually proposed to optimize BOE to avoid the counter-intuitive fusion results. In this work, inspired by the complex network, a body of evidence is compared to a node, therefore multiple nodes composed of the BOEs constitute a complex network structure, and a correlation coefficient is adopted to measure the degree of correlation between two BOEs. The direct and indirect interaction weights of each node are determined through the direct and indirect interactions among the nodes to reflect their importance in the complex network. After that, the total weight of each BOE is calculated through using the direct and indirect weights. Finally, after modifying the original BOE with weight factor, the final result is obtained after information fusion by using Dempster’s combination rule. This work analyses a practical application case based on the proposed evidential-weighting complex networks in D–S theory. The experiment result shows that the complex network optimization algorithm proposed in this work possesses a good convergence and has significantly improved the counter-intuitive fusion results brought about by the highly conflicting evidence with Dempster’s combination rule.

Suggested Citation

  • Tang, Yongchuan & Dai, Guoxun & Zhou, Yonghao & Huang, Yubo & Zhou, Deyun, 2023. "Conflicting evidence fusion using a correlation coefficient-based approach in complex network," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
  • Handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923009888
    DOI: 10.1016/j.chaos.2023.114087
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923009888
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2023.114087?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yifan Liu & Tiantian Bao & Huiyun Sang & Zhaokun Wei, 2021. "A Novel Method for Conflict Data Fusion Using an Improved Belief Divergence Measure in Dempster–Shafer Evidence Theory," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, October.
    2. Du, Yuxian & Lin, Xi & Pan, Ye & Chen, Zhaoxin & Xia, Huan & Luo, Qian, 2023. "Identifying influential airports in airline network based on failure risk factors with TOPSIS," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    3. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sepestanaki, Mohammadreza Askari & Rezaee, Hamidreza & Soofi, Mohammad & Fayazi, Hossein & Rouhani, Seyed Hossein & Mobayen, Saleh, 2024. "Adaptive continuous barrier function-based super-twisting global sliding mode stabilizer for chaotic supply chain systems," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samuel Ugwu & Pierre Miasnikof & Yuri Lawryshyn, 2023. "Distance Correlation Market Graph: The Case of S&P500 Stocks," Mathematics, MDPI, vol. 11(18), pages 1-13, September.
    2. Teqi Dai & Tiantian Ding & Qingfang Liu & Bingxin Liu, 2022. "Node Centrality Comparison between Bus Line and Passenger Flow Networks in Beijing," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    3. Shen, Jingwei & Zong, Huiming, 2023. "Identification of critical transportation cities in the multimodal transportation network of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    4. Bao, Tiantian & Liu, Yifan & Yang, Zhongzhen & Wu, Shanhua & Yan, Zhenli, 2024. "Evaluating sustainable service quality in higher education from a multi-stakeholder perspective: An integrated fuzzy group decision-making method," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    5. Song, Lili & Wu, Yingying & Wu, Moyu & Ma, Jie & Cao, Wei, 2023. "An integrated approach to model connectivity and identify modules for habitat networks," Ecological Modelling, Elsevier, vol. 483(C).
    6. Meng, Yangyang & Zhao, Xiaofei & Liu, Jianzhong & Qi, Qingjie & Zhou, Wei, 2023. "Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923009888. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.