Optimizing SMS Spam Detection: Leveraging the Strength of a Voting Classifier Ensemble - N Sardhak Manikanta, G Hari Surya Bharadwaj, P Siva Krishna Teja, G Rama Koteswara Rao, Manas Ranjan Bishi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The paper addresses the challenge of SMS spam, which has seen a significant rise in recent years. This work proposes an ensemble learning` approach using Support Vector Machine (SVM), Naive Bayes, Extra Trees, and a Voting Classifier to enhance SMS spam detection. The system employs diverse machine learning algorithms, meticulously chosen and fine-tuned for optimal performance. The ensemble, centered on the Voting Classifier strengthened by a Random Forest Classifier, plays a crucial role in identifying spam messages. Evaluation is conducted using key metrics such as Accuracy, Precision, Recall, and F1-score to provide a comprehensive understanding of the model's effectiveness. Dataset exploration revealed unexpected dynamics, challenging initial assumptions. For instance, higher word counts were observed in spam messages, potentially reflecting strategic tactics employed by spammers. Additionally, the identification of over 6,000 duplicate texts within spam messages raises intriguing questions about spammers' methodologies. The development process incorporates meticulous data preprocessing steps like tokenization, lowercasing, and stop word removal. Rigorous training sessions with SVM, Naive Bayes, and Random Forest classifiers leverage their unique strengths, while the introduction of a voting ensemble method enhances the model's robustness and mitigates potential biases. The paper concludes by demonstrating the practical application of the SMS spam detector, achieving an accuracy of 94% through the combined application of various machine learning algorithms. This systematic and thoughtful approach positions the paper as a valuable contribution to the field of SMS spam detection, addressing real-world challenges in digital communication security.