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Analysis of Spam Classification Based on Naive Bayes and Random Forest Model
- 1 Department of Computer Science, Guangxi University of Science and Technology, Liuzhou, China
* Author to whom correspondence should be addressed.
Abstract
Spam classification has become more and more significant in email filtering and content auditing systems nowadays. Despite the development of many ways for filtering spam, spammers continue to adopt new methods for spam detection, which has left us overwhelmed with spam. Furthermore, robust, and flexible categorization algorithms are necessary to keep up with the constant evolution of spam tactics. The best method for categorizing and filtering spam now is to use machine learning techniques. In this study, a large spam dataset containing 5572 email instances is used in simulations for the spam classification task. This study comparatively analyzes two prevalent machine learning algorithms, namely, Random Forest and Naive Bayes. A detailed description of both algorithms, including their theoretical foundations and practical implementations in spam detection, is provided. In addition, the data was characterized in the study for training the models as well as making predictions. Finally, the effectiveness and performance of each algorithm is shown in the experimental evaluation using four commonly used performance evaluation metrics. Overall, these results providing insights into their strengths and limitations in practical spam filtering applications.
Keywords
Spam classification, Naive Bayes, Random Forest, Performance evaluation indicators, feature engineering
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Cite this article
Li,K. (2024). Analysis of Spam Classification Based on Naive Bayes and Random Forest Model. Advances in Economics, Management and Political Sciences,84,250-257.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Management Research and Economic Development
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