An Investigation of Studies on Spam Filtering Based on Machine Learning

Yalu Wang

2024

Abstract

With the development of network communication, mail is one of the most widely used means of communication. But a lot of spam also followed, to the users and mail platform caused a lot of trouble. The common approach to filter spam is divided into six steps, and, respectively, the data collection, preprocessing, model building, model training and model testing, deployment. This paper also introduces logistic regression, decision tree, random forest, deep learning models and Atomic Orbital Search (AOS) algorithm in detail. Models such as Genetic Decision Tree Processing with Natural Language Processing (GDTPNLP) and Area Under curve (AUC) use machine learning to deal with spam. The logistic regression machine learning method is a machine learning technique and usually used to classify emails. Decision tree processing (DT) is a significant and effective machine learning model, widely employed as a predictive analysis technique for classification purposes. Random Forest is an ensemble learning classification and regression method for dealing with problems that involve grouping data into classes. In the research of electronic spam, some methods still have some limitations. The development of more efficient technologies is crucial in effectively addressing trends or advancements in spam features.

Paper Citation

in Harvard Style

Wang Y. (2024). An Investigation of Studies on Spam Filtering Based on Machine Learning. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 790-793. DOI: 10.5220/0012973400004508

in Bibtex Style

@conference{emiti24,
author={Yalu Wang},
title={An Investigation of Studies on Spam Filtering Based on Machine Learning},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={790-793},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012973400004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - An Investigation of Studies on Spam Filtering Based on Machine Learning
SN - 978-989-758-713-9
AU - Wang Y.
PY - 2024
SP - 790
EP - 793
DO - 10.5220/0012973400004508
PB - SciTePress