SMS Spam Detection using Relevance Vector Machine

Abstract

Spam in Short Message Service(SMS) is a serious issue that impacts mobile phone consumers all around the world. Many strategies have been applied using several deep learning and machine learning techniques to overcome these issues. The bagging approach is used in the study to combine four different algorithms, namely RVM, SVM, Naive Bayes, and KNN. Then the final prediction is calculated from the predictions obtained from each of these algorithms by using a majority-based voting approach. So, this paper offers research on the comparative analysis of various text classification algorithms for accurately detecting and classifying spam SMS messages. The dataset is first preprocessed and then vectorized using the TF-IDF method which gives more importance to the less frequent words rather than common words. The Relevance vector machine (RVM) implementation on the dataset, achieves the best performance on this dataset with an F1 score of 0.975175. According to the study's findings, the suggested RVM model may successfully categorize SMS spam messages and be applied in practical settings.