Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique

Ha Che-Ngoc (), Thao Nguyen-Trang (), Hieu Huynh- Van () and Tai Vo- Van ()
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Ha Che-Ngoc: Ton Duc Thang University
Thao Nguyen-Trang: Van Lang University
Hieu Huynh- Van: Ho Chi Minh City University of Technology (HCMUT)
Tai Vo- Van: Can Tho University

Annals of Data Science, 2024, vol. 11, issue 2, No 13, 709-732

Abstract: 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.

Keywords: Bayesian method; Bayes error; Prior probability; Vine Copulas (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00490-4

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