Email Spam Filtering Methods: Comparison and Analysis

Abstract

Email is a common way of communication due to its cheap cost, efficacy, and efficiency. With the emerging of deep learning and machine learning methods, spam filter classification achieves boosting performance with fast inference speed. However, individuals and email servers are affected by spam, which causes issues with wasted time and computer storage space, as well as adverse effects on bandwidth. Even worse, email users are susceptible to scams and fraud that may result in financial loss. Therefore, it is essential to discover an efficient approach to filter spam from the entire number of emails. The purpose of this study is to evaluate and contrast the five most popular machine learning based spam filtering techniques, including Naive Bayes, Supported Vector Machine K-Nearest Neighbor, and XGBoost. We evaluate them based on their performance and efficacy. We hope this paper will help to conclude the current condition and help the researchers to improve better algorithms with higher accuracy.