Enhanced SMS spam classification using machine learning with optimized hyperparameters
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Enhanced SMS spam classification using machine learning with optimized hyperparameters |
| 2. | Creator | Author's name, affiliation, country | Nasreddine Hafidi; Sultan Moulay Slimane University; Morocco |
| 2. | Creator | Author's name, affiliation, country | Zakaria Khoudi; Sultan Moulay Slimane University; Morocco |
| 2. | Creator | Author's name, affiliation, country | Mourad Nachaoui; Sultan Moulay Slimane University; Morocco |
| 2. | Creator | Author's name, affiliation, country | Soufiane Lyaqini; Hassan First University; Morocco |
| 3. | Subject | Discipline(s) | machine learning, optimization, neural computing |
| 3. | Subject | Keyword(s) | Classification; Genetic algorithm; Hyperparameter tuning; SMS classification; Spam detection; Supervised learning |
| 4. | Description | Abstract | Short message service (SMS) text messages are indispensable, but they face a significant issue with spam. Therefore, there is a need for robust models capable of classifying SMS messages as spam or non-spam. Machine learning offers a promising approach for this classification, based on existing datasets. This study explores a comparison of several techniques, including logistic regression (LR), support vector machines (SVM), gradient boosting (GB), and neural networks (NN). Hyperparameters play a crucial role in the performance of these models, and their optimization is essential for achieving high accuracy. To this end, we employ an evolutionary programming approach for hyperparameter optimization. This approach evaluates the performance of these models before and after hyperparameter optimization, aiming to identify the most effective model for SMS spam classification. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | FST, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco |
| 7. | Date | (YYYY-MM-DD) | 2025-01-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/38797 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v37.i1.pp356-364 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 37, No 1: January 2025 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2024 Nasreddine HAFIDI This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. |