IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v11y2024i2d10.1007_s40745-023-00490-4.html
   My bibliography  Save this article

Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique

Author

Listed:
  • Ha Che-Ngoc

    (Ton Duc Thang University)

  • Thao Nguyen-Trang

    (Van Lang University
    Van Lang University)

  • Hieu Huynh-Van

    (Ho Chi Minh City University of Technology (HCMUT)
    Vietnam National University Ho Chi Minh City
    Industrial University of Ho Chi Minh City)

  • Tai Vo-Van

    (Can Tho University)

Abstract

Classification is a fundamental problem in statistics and data science, and it has garnered significant interest from researchers. This research proposes a new classification algorithm that builds upon two key improvements of the Bayesian method. First, we introduce a method to determine the prior probabilities using fuzzy clustering techniques. The prior probability is determined based on the fuzzy level of the classified element within the groups. Second, we develop the probability density function using Vine Copula. By combining these improvements, we obtain an automatic classification algorithm with several advantages. The proposed algorithm is presented with specific steps and illustrated using numerical examples. Furthermore, it is applied to classify image data, demonstrating its significant potential in various real-world applications. The numerical examples and applications highlight that the proposed algorithm outperforms existing methods, including traditional statistics and machine learning approaches.

Suggested Citation

  • Ha Che-Ngoc & Thao Nguyen-Trang & Hieu Huynh-Van & Tai Vo-Van, 2024. "Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique," Annals of Data Science, Springer, vol. 11(2), pages 709-732, April.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:2:d:10.1007_s40745-023-00490-4
    DOI: 10.1007/s40745-023-00490-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-023-00490-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-023-00490-4?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Hong Qiu & Genhua Hu & Yuhong Yang & Jeffrey Zhang & Ting Zhang, 2020. "Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    2. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    3. Hanene MEJDOUB & Mounira BEN ARAB, 2017. "A Multivariate Analysis for Risk Capital Estimation in Insurance Industry: Vine Copulas," Asian Development Policy Review, Asian Economic and Social Society, vol. 5(2), pages 100-119, June.
    4. Marcus Scheffer & Gregor N. F. Weiß, 2020. "Extreme dependence in investor attention and stock returns – consequences for forecasting stock returns and measuring systemic risk," Quantitative Finance, Taylor & Francis Journals, vol. 20(3), pages 425-446, March.
    5. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    6. Dezhao Han & Ken Seng Tan & Chengguo Weng, 2017. "Vine copula models with GLM and sparsity," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(13), pages 6358-6381, July.
    7. Hanène MEJDOUB & Mounira BEN ARAB, 2017. "A Multivariate Analysis for Risk Capital Estimation in Insurance Industry: Vine Copulas," Asian Development Policy Review, Asian Economic and Social Society, vol. 5(2), pages 100-119.
    8. Dalu Zhang & Meilan Yan & Andreas Tsopanakis, 2018. "Financial stress relationships among Euro area countries: an R-vine copula approach," The European Journal of Finance, Taylor & Francis Journals, vol. 24(17), pages 1587-1608, November.
    9. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    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. Che, Yulong & Wang, Xiaoru & Ge, Leijiao & Lin, Hongjian & Lyu, Xiaoqin & Su, Hongsheng & Wang, Hao, 2025. "A review of research on traction load models and modeling methods for electrified railways," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(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. F. Durante & A. Gatto & F. Ravazzolo, 2024. "Understanding relationships with the Aggregate Zonal Imbalance using copulas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 513-554, April.
    2. Yao, Yinhong & Chen, Xiuwen & Chen, Zhensong, 2025. "Portfolio tail risk forecasting for international financial assets: A GARCH-MIDAS-R-Vine copula model," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    3. Nagler, Thomas & Czado, Claudia, 2016. "Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 69-89.
    4. Benoumechiara Nazih & Bousquet Nicolas & Michel Bertrand & Saint-Pierre Philippe, 2020. "Detecting and modeling critical dependence structures between random inputs of computer models," Dependence Modeling, De Gruyter, vol. 8(1), pages 263-297, January.
    5. Talbi, Marwa & Bedoui, Rihab & de Peretti, Christian & Belkacem, Lotfi, 2021. "Is the role of precious metals as precious as they are? A vine copula and BiVaR approaches," Resources Policy, Elsevier, vol. 73(C).
    6. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
    7. Song, Yupeng & Basu, Biswajit & Zhang, Zili & Sørensen, John Dalsgaard & Li, Jie & Chen, Jianbing, 2021. "Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method," Renewable Energy, Elsevier, vol. 168(C), pages 991-1014.
    8. Nagler Thomas & Schellhase Christian & Czado Claudia, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
    9. Anthony N. Rezitis & Andreas Rokopanos, 2019. "Impact of trade liberalisation on dairy market price co‐movements between the EU, Oceania, and the United States," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(3), pages 472-498, July.
    10. Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.
    11. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of energy prices on clean energy stock prices. A multivariate quantile dependence approach," Energy Economics, Elsevier, vol. 76(C), pages 136-152.
    12. Guo, Rui & Shamsi, Mohammad Haris & Sharifi, Mohsen & Saelens, Dirk, 2025. "Exploring uncertainty in district heat demand through a probabilistic building characterization approach," Applied Energy, Elsevier, vol. 377(PA).
    13. Himchan Jeong & Dipak Dey, 2020. "Application of a Vine Copula for Multi-Line Insurance Reserving," Risks, MDPI, vol. 8(4), pages 1-23, October.
    14. Weiß, Gregor N.F. & Scheffer, Marcus, 2015. "Mixture pair-copula-constructions," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 175-191.
    15. Chang, Bo & Joe, Harry, 2019. "Prediction based on conditional distributions of vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 45-63.
    16. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    17. Brechmann, Eike C. & Hendrich, Katharina & Czado, Claudia, 2013. "Conditional copula simulation for systemic risk stress testing," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 722-732.
    18. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    19. Saha, Kunal, 2018. "An investigation into the dependence structure of major cryptocurrencies," EconStor Preprints 181878, ZBW - Leibniz Information Centre for Economics.
    20. Jose Arreola Hernandez & Shawkat Hammoudeh & Duc Khuong Nguyen & Mazin A. M. Al Janabi & Juan Carlos Reboredo, 2017. "Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2409-2427, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:aodasc:v:11:y:2024:i:2:d:10.1007_s40745-023-00490-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.