Abstract
The widespread use of Electronic Mail (E-mail) has led to a significant increase in spam, which has severely impeded the growth and well-being of the Internet. To mitigate this issue, the implementation of email filtering techniques has become necessary, requiring the use of specific technological tools. Presently, the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) algorithms are commonly used in probability statistical classification methods for email filtering. Among these, the NB algorithm is the most classical, with its rich mathematical theory as the basis, high classification efficiency, and straightforward algorithmic approach. However, the algorithm relies on the conditional independence assumption, making the accuracy susceptible to the correlation between attributes. This study focuses on email filtering techniques based on the NB algorithm, conducting experiments to evaluate the classification accuracy and proposing feasible improvements to weaken the independence assumption. The experimental results demonstrated the effectiveness of the employed method.
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