[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123031-en":3,"doc-seo-123031-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123031,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhanced SMS spam classification using machine learning with optimized hyperparameters - Article","Short message service (SMS) spam disrupts communication and can lead to financial harm, making reliable spam filtering essential. The study evaluates supervised machine-learning methods—logistic regression, support vector machines, gradient boosting, and neural networks—on existing datasets to classify SMS messages as spam or non-spam. Because hyperparameters strongly affect accuracy, evolutionary programming is used to optimize hyperparameters and compare model performance before and after tuning, selecting the most effective approach for SMS spam detection.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 37, No. 1, January 2025, pp. 356∼364  \nISSN: 2502-4752, DOI: 10.11591/ijeecs.v37.i1.pp356-364 ❒ 356  \n\n| Enhanced SMS spam classification using machine learning with optimized hyperparameters\u003Cbr>Nasreddine Hafidi1 , Zakaria Khoudi1 , Mourad Nachaoui1 , Soufiane Lyaqini2\u003Cbr>1Equipe Mathmatiques et Interactions, Facult des sciences et Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco\u003Cbr>2Ecole Nationale des Sciences Appliquees, LAMSAD Laboratory, Hassan First University, Settat, Morocco |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Jun 2, 2024 Revised Sep 8, 2024 Accepted Sep 29, 2024\u003Cbr>Keywords:\u003Cbr>Classification Genetic algorithm Hyperparameter tuning SMS classification Spam detection Supervised learning |  | ABSTRACT\u003Cbr>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.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| Nasreddine Hafidi\u003Cbr>Equipe Mathmatiques et Interactions, Facult des sciences et Techniques, Sultan Moulay Slimane University Beni Mellal 23000, Morocco\u003Cbr>Email: nasreddine.hafidi@usms.ma |  |  |\n\n1. INTRODUCTION  \nShort message service (SMS) text messages are indispensable in modern communication, yet they face a significant challenge from spam messages. In general, spammers use these messages to promote their utilities or businesses. Spam messages can be annoying and, in some cases, harmful to recipients. Sometimes, users can also suffer financial losses due to these spam messages, making it crucial to develop robust models that can effectively classify SMS messages as spam or non-spam. Machine learning provides a promising approach to tackle this problem by leveraging existing datasets to train models for accurate classification [1], especially for spam classification [2] .  \nNumerous machine-learning methods can be used for SMS spam classification. Each method has strengths and weaknesses, and choosing the most appropriate algorithm is challenging. Additionally, the performance of these algorithms can be highly dependent on the proper tuning of hyperparameters. Despite the variety of comparative studies, adjusting hyperparameters can lead to substantial changes in algorithm performance, adding another layer of complexity to the problem.  \nThis study aims to develop a robust machine-learning architecture for classifying SMS messages into spam or non-spam. We will compare various machine learning techniques, including logistic regression (LR)  \n[3], support vector machines (SVM) [4], random forest (RF) [5] and gradient boosting (GB) [6], and to select the most suitable model for optimal results. The performance of each model relies heavily on a set of hyperpa-  \nrameters, which play a crucial role in influencing the results. Even small changes in these hyperparameters can significantly change the model’s performance and the overall set of hyperparameters.  \nTo ensure the model’s robustness and optimal performance, we will employ evolutionary programming [7] for hyperparameter optimization. This technique will iteratively sear","cbCaivbYfWAUWJhD","https://ap.wps.com/l/cbCaivbYfWAUWJhD","pdf",670631,1,9,"English","en",105,"# Introduction\n## Setting of the Problem\n# Methodology and Proposed Approach\n## Hyperparameter Optimization via Evolutionary Programming\n# Experimental Results and Discussion\n## Model Comparison With and Without Optimization\n# Conclusion","[{\"question\":\"Why is SMS spam classification important?\",\"answer\":\"SMS spam disrupts modern communication and may cause annoyance or financial losses for recipients, so robust spam vs. non-spam classification is needed.\"},{\"question\":\"Which machine-learning techniques are compared in the study?\",\"answer\":\"The paper compares logistic regression, support vector machines, random forest, and gradient boosting, alongside neural networks within the overall evaluation framework.\"},{\"question\":\"How does the study improve model performance?\",\"answer\":\"It uses evolutionary programming to optimize hyperparameters, then compares each model’s accuracy before and after optimization to identify the best configuration.\"}]","Enhanced SMS spam classification using machine learning with optimized hyperparameters - 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