Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Savia Berrou; Khadija Al Kalbani; Milos Antonijevic; Miodrag Zivkovic; Nebojsa Bacanin; Bosko Nikolic

doi:10.2991/978-94-6463-110-4_22

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Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Authors

Savia Berrou¹, Khadija Al Kalbani¹, Milos Antonijevic², Miodrag Zivkovic²^{, *}, Nebojsa Bacanin², Bosko Nikolic³

¹Modern College of Business and Science, Muscat, Oman

²Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

³School of Electrical Engineering, Belgrade University, Bulevar Kralja Aleksandra 73, 11000, Belgrade, Serbia

^*Corresponding author. Email: mzivkovic@singidunum.ac.rs

Corresponding Author

Miodrag Zivkovic

Available Online 30 January 2023.

DOI: 10.2991/978-94-6463-110-4_22 How to use a DOI?
Keywords: Logistic regression; TLBO algorithm; Spam email detection
Abstract: Spam and emails have always been intrinsically linked since the creation of the Advanced Research Projects Agency Network, otherwise known as (ARPANET). The latter witnessed, on May 3rd, 1978, the first known spam email to date. Today, spam emails negatively affect the users’ productivity and private lives. A significant number of approaches emerged in the past two decades that deal with the spam detection problem, with limited success. Therefore, the current paper presents an intelligent and automated solution to spam email detection using a logistic regression model trained by a teaching-learning-based optimization algorithm. The proposed solution has been tested on two benchmark spam email datasets (CSDMC2010 and TurkishEmail), and evaluated against seven other contending cutting-edge metaheuristics utilized in the same experimental setup. The simulation outcomes without a doubt indicate the superior level of accuracy achieved by the proposed solution.
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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Volume Title: Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
Series: Advances in Computer Science Research
Publication Date: 30 January 2023
ISBN: 978-94-6463-110-4
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-110-4_22 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Savia Berrou
AU  - Khadija Al Kalbani
AU  - Milos Antonijevic
AU  - Miodrag Zivkovic
AU  - Nebojsa Bacanin
AU  - Bosko Nikolic
PY  - 2023
DA  - 2023/01/30
TI  - Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm
BT  - Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
PB  - Atlantis Press
SP  - 306
EP  - 327
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-110-4_22
DO  - 10.2991/978-94-6463-110-4_22
ID  - Berrou2023
ER  -

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