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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
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