IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v213y2021ics0951832021003082.html
   My bibliography  Save this article

Reliability evaluation of two-stage evidence classification system considering preference and error

Author

Listed:
  • Liu, Qiang

Abstract

In classic weighted voting systems (WVSs) and weighted voting classifiers (WVCs), the uncertainty in the system is characterized by probability theory, and the decisions of voting units are fused according to the majority rule. We use D-S evidence theory to improve the representation of the voting unit's decision and the fusion rule in WVSs and WVCs, and propose a new evidence classification system (ECS). In the ECS, a voting unit's decision is represented as a basic probability assignment function, the majority rule is replaced by the combination rule in D-S evidence theory. We also extend the WVS or WVC's decision-making process into two stages, and consider the measurement error and decision preference of the voting unit. A method based on Monte Carlo simulation is proposed to evaluate the reliability of the ECS. The effects of the number of voting units, decision preference and measurement error on the system reliability are also analyzed. Simulation results show that the introduction of evidence theory into ECS can effectively eliminate the effect of preference and error on system reliability.

Suggested Citation

  • Liu, Qiang, 2021. "Reliability evaluation of two-stage evidence classification system considering preference and error," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
  • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021003082
    DOI: 10.1016/j.ress.2021.107783
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021003082
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2021.107783?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. Saba Bashir & Usman Qamar & Farhan Khan, 2015. "Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2061-2076, September.
    2. Zhang, Xiaoge & Mahadevan, Sankaran & Deng, Xinyang, 2017. "Reliability analysis with linguistic data: An evidential network approach," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 111-121.
    3. N. H. Bingham, 1990. "An Introduction to the Theory of Coverage Processes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(2), pages 257-258, March.
    4. Simon, C. & Weber, P. & Evsukoff, A., 2008. "Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 950-963.
    5. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    6. Yu-ke Chen & Yan Zou & Zhe Chen, 2014. "Preference Integration and Optimization of Multistage Weighted Voting System Based on Ordinal Preference," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-6, May.
    7. Beynon, Malcolm, 2002. "DS/AHP method: A mathematical analysis, including an understanding of uncertainty," European Journal of Operational Research, Elsevier, vol. 140(1), pages 148-164, July.
    8. Long, Q. & Xie, M. & Ng, S.H. & Levitin, Gregory, 2008. "Reliability analysis and optimization of weighted voting systems with continuous states input," European Journal of Operational Research, Elsevier, vol. 191(1), pages 240-252, November.
    9. Levitin, Gregory, 2002. "Evaluating correct classification probability for weighted voting classifiers with plurality voting," European Journal of Operational Research, Elsevier, vol. 141(3), pages 596-607, September.
    10. Yong Zhang & Hongrui Zhang & Jing Cai & Binbin Yang, 2014. "A Weighted Voting Classifier Based on Differential Evolution," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-6, May.
    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. Sezer, Sukru Ilke & Akyuz, Emre & Arslan, Ozcan, 2022. "An extended HEART Dempster–Shafer evidence theory approach to assess human reliability for the gas freeing process on chemical tankers," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Liu, Qiang & Zhang, Hailin, 2022. "Reliability evaluation of weighted voting system based on D–S evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 217(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. Liu, Qiang & Zhang, Hailin, 2022. "Reliability evaluation of weighted voting system based on D–S evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Tingnan Lin & Hoang Pham, 2022. "A two-stage intervened decision system with multi-state decision units and dynamic system configuration," Annals of Operations Research, Springer, vol. 311(1), pages 255-277, April.
    3. Chunyan, Ling & Jingzhe, Lei & Way, Kuo, 2022. "Bayesian support vector machine for optimal reliability design of modular systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. Arán Carrión, J. & Espín Estrella, A. & Aznar Dols, F. & Zamorano Toro, M. & Rodríguez, M. & Ramos Ridao, A., 2008. "Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2358-2380, December.
    6. Felipe Aguirre & Mohamed Sallak & Walter Schön & Fabien Belmonte, 2013. "Application of evidential networks in quantitative analysis of railway accidents," Journal of Risk and Reliability, , vol. 227(4), pages 368-384, August.
    7. Limbourg, Philipp & de Rocquigny, Etienne, 2010. "Uncertainty analysis using evidence theory – confronting level-1 and level-2 approaches with data availability and computational constraints," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 550-564.
    8. Yeh, Wei-Chang, 2017. "Evaluation of the one-to-all-target-subsets reliability of a novel deterioration-effect acyclic multi-state information network," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 132-137.
    9. A.H.T. Shyam Kularathna & Sayaka Suda & Ken Takagi & Shigeru Tabeta, 2019. "Evaluation of Co-Existence Options of Marine Renewable Energy Projects in Japan," Sustainability, MDPI, vol. 11(10), pages 1-26, May.
    10. Sultan Almotairi & Elsayed Badr & Mustafa Abdul Salam & Hagar Ahmed, 2023. "Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
    11. Jorge L. García-Alcaraz & Aidé A. Maldonado-Macías & Juan L. Hernández-Arellano & Julio Blanco-Fernández & Emilio Jiménez-Macías & Juan C. Sáenz-Díez Muro, 2016. "Agricultural Tractor Selection: A Hybrid and Multi-Attribute Approach," Sustainability, MDPI, vol. 8(2), pages 1-16, February.
    12. Wang, Yong & Li, Lin & Huang, Shuhong & Chang, Qing, 2012. "Reliability and covariance estimation of weighted k-out-of-n multi-state systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 138-147.
    13. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling & Chin, Kwai-Sang, 2006. "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees," European Journal of Operational Research, Elsevier, vol. 175(1), pages 35-66, November.
    14. Meshwa Rameshbhai Savalia & Jaiprakash Vinodkumar Verma, 2023. "Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 12(1), pages 1-19, January.
    15. Ozdemir, Mujgan S. & Saaty, Thomas L., 2006. "The unknown in decision making: What to do about it," European Journal of Operational Research, Elsevier, vol. 174(1), pages 349-359, October.
    16. Jiang, Chen & Yan, Yifang & Wang, Dapeng & Qiu, Haobo & Gao, Liang, 2021. "Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    17. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system considering common cause failure: Based on a beta-factor and continuous-time bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    18. Narimah Samat & Mohd Amirul Mahamud & Mou Leong Tan & Mohammad Javad Maghsoodi Tilaki & Yi Lin Tew, 2020. "Modelling Land Cover Changes in Peri-Urban Areas: A Case Study of George Town Conurbation, Malaysia," Land, MDPI, vol. 9(10), pages 1-16, October.
    19. Sheibani, Mohamadreza & Ou, Ge, 2021. "Adaptive local kernels formulation of mutual information with application to active post-seismic building damage inference," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    20. Justin Moskolaï Ngossaha & Raymond Houé Ngouna & Bernard Archimède & Mihaela-Hermina Negulescu & Alexandru-Ionut Petrişor, 2024. "Toward Sustainable Urban Mobility: A Multidimensional Ontology-Based Framework for Assessment and Consensus Decision-Making Using DS-AHP," Sustainability, MDPI, vol. 16(11), pages 1-22, May.

    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:reensy:v:213:y:2021:i:c:s0951832021003082. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.