Towards a Comprehensive Comparative Evaluation of Classical Machine Learning Algorithms for SMS Spam Classification Using TF-IDF Representations

Author(s)	Ms. DURGA J, Ms. SUNITHA S
Country	India
Abstract	Detecting spam is a crucial task in today's communication systems, given the rising number of unwanted messages that can threaten security and privacy. This research offers a comparative evaluation of four machine-learning methods—Multinomial Naive Bayes, Logistic Regression, Support Vector Machine (SVM), and Random Forest—for classifying SMS spam. The SMS Spam Collection dataset underwent pre-processing and transformation through the Term Frequency–Inverse Document Frequency (TF-IDF) approach to turn text data into numerical feature vectors.
Keywords	Naïve Bayes, Support Vector Machine(SVM), Frequency-Inverse- Document Frequency(TF-IDF), F1-Score, Logistic Regression, Random Forest
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-30
DOI	https://doi.org/10.71097/IJSAT.v16.i4.10013
Short DOI	https://doi.org/hbhj57

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International Journal on Science and Technology