Abstract
Billions of messages are sent daily over the Internet, out of which a majority part of them is spam. These spam messages have become a primary cause of distraction and security threat for users as their number keeps on increasing day by day. Many researchers have addressed this problem, and there are different approaches to it. In this present study, Machine Learning algorithms such as Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, Gradient Boosting, and Extra Trees Classifier have been utilized to predict whether an incoming message or e-mail is spam. The model performance has been evaluated based on accuracy, precision, F1-score, and confusion matrix. Three different datasets including two SMS datasets and one e-mail dataset has been used, and a maximum F1-score of 96.06% and accuracy of 99.12% with the Extra Trees Classifier are achieved, which is 0.02% higher than the highest value of accuracy ever achieved for the SMS Spam Collection Dataset.
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Bari, P., Mathew, V., Tandel, S.P., Aniket, P., Chaudhari, K.S., Naik, S. (2023). SMS and E-mail Spam Classification Using Natural Language Processing and Machine Learning. In: Singh, S.N., Mahanta, S., Singh, Y.J. (eds) Proceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology. NICE-DT 2023. Lecture Notes in Networks and Systems, vol 676. Springer, Singapore. https://doi.org/10.1007/978-981-99-1699-3_6
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