Analysis and comparison of machine learning methods and improved SVM algorithm in spam classification

Hongda Zhu

doi:10.1117/12.2674952

23 May 2023 Analysis and comparison of machine learning methods and improved SVM algorithm in spam classification

Hongda Zhu

Author Affiliations +

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126044Q (2023) https://doi.org/10.1117/12.2674952
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

The most popular form of official communication for business purposes is email. Despite the existence of other communication methods, email usage is still the largest. Today's environment necessitates automated email management due to the daily increase in email volume. More than 55% of emails users received nowadays are flagged as spam. This exemplifies how these spams squander the time and resources of email users while creating nothing beneficial. Understanding the various spam email categorization strategies and how they operate is essential since spammers employ complex and creative techniques to carry out their illicit operations through spam emails. The comparison to find the most accuracy machine learning-based spam categorization methods such Naïve Bayes, SVM, and random forest is the initial objective of this work, after that the paper compares the initial result with the improved SVM algorithm. This study provides a comprehensive analysis and assessment of earlier studies on various machine learning methods, email properties, and methodologies. The results show that the improved support vector machine obtains a good email classification effect and can meet the requirements of spam processing Introduction

Citation Download Citation

Hongda Zhu "Analysis and comparison of machine learning methods and improved SVM algorithm in spam classification", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126044Q (23 May 2023); https://doi.org/10.1117/12.2674952

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