Research on Spam Filters using: SVM, Naïve Bayes, and KNN

DOI

10.2991/978-94-6463-300-9_59How to use a DOI?

Keywords

Spam filtering; Spam classification; SVM; KNN; Naïve Bayes

Abstract

Email becomes a main way for people to communicate or send information to each other. However, spammers send people unwanted and harmful information using emails. Therefore, useful email filtering needs to be used for our email. This paper shows a comprehensive review and comparative concept of various spam filtering techniques by highlighting their strengths, weaknesses, and performance. The study focuses on three prominent approaches: K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machines (SVM). A large dataset of emails is used to determine how well each classifier performs. The testing set and the training set are two separate portions of the dataset. The computation of a number of performance metrics will be used. The performance metrics includes the precision, accuracy, f1-score, and recall of the specific filter. The analysis’s findings show each technique’s advantages and disadvantages. SVM exhibits great precision and accuracy but may be susceptible to parameter tuning and feature selection. KNN achieves competitive results with a straightforward implementation but can suffer from scalability issues. Naïve Bayes, despite its simplistic assumptions, performs well too.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yiting Wang
PY  - 2023
DA  - 2023/11/27
TI  - Research on Spam Filters using: SVM, Naïve Bayes, and KNN
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 574
EP  - 580
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_59
DO  - 10.2991/978-94-6463-300-9_59
ID  - Wang2023
ER  -