Enhanced SMS spam classification using machine learning with optimized hyperparameters

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.