Lee-Kien Foo*, Sook-Ling Chua, Neveen Ibrahim
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1945-1957, 2022, DOI:10.32604/cmc.2022.022011
Abstract The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time. One approach… More >
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