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Spam filter based on naive bayes algorithm
- 1 Dalian University of Technology
* Author to whom correspondence should be addressed.
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.
Keywords
spam filter, naive bayes algorithm, machine learning
[1]. Malakoff D A 1999 Brief Guide to Bayes Theorem Science 286(5444) 1461-1461
[2]. Zhang K Chen X Song Y et al. 2019 improved method for TAN method Computing technology and Automation (in Chinese) (01):55-61
[3]. Lin S Tian F 2000 Research on Bayesian classifier for data mining Computer Science (in Chinese) 27(10) 73-76
[4]. Lu L Wang J Wang C 2017 Research on data mining Classification Algorithm for cloud computing Microcomputers and applications (in Chinese) 36(06):7-9
[5]. Ma G 2018 The improvement and application of Naive Bayes Algorithm (in Chinese) Hefei: Anhui University
[6]. Wang L 2020 Research on spam filtering based on Bayesian (in Chinese) Shanghai: Shanghai University of Engineering Science
[7]. Liu Y Xing Y 2019 Research and application of text classification based on improved random forest algorithm (in Chinese) Computer system application 28(05):222-227
[8]. Breiman L 2001 Random Forests Machine Learning 45(01):5-32
[9]. Wang Y Xia S 2018 A survey of random forest algorithms for ensemble learning Information and Communication Technology (in Chinese) 12(01):49-55
[10]. Biau G Scornet E 2016 A random forest guided tour Test 25: 197-227
Cite this article
Han,M. (2023). Spam filter based on naive bayes algorithm. Applied and Computational Engineering,15,247-252.
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 5th International Conference on Computing and Data Science
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