SMS Spam Detection using Relevance Vector Machine

• DOI: 10.1016/j.procs.2023.12.089
• Publication Date: 2024-01-06T16:53:23Z
• Authors: Shushanta Pudasaini; Aman Shakya; Sanjeeb Prasad Pa...; Prakriti Paudel; Sunil Ghimire; Prabhat Ale
• Keywords: Relevance; Bag-of-words model; Text Categorization; Relevance vector machine

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 techniques to overcome these issues. The bagging approach used study combine four different algorithms, namely RVM, SVM, Naive Bayes, KNN. Then final prediction calculated from predictions obtained each of algorithms by majority-based voting approach. So, this paper offers research on comparative analysis various text classification for accurately detecting classifying spam SMS messages. dataset first preprocessed then vectorized TF-IDF method which gives more importance less frequent words rather than common words. Relevance vector (RVM) implementation dataset, achieves best performance with an F1 score 0.975175. According study's findings, suggested RVM model may successfully categorize messages be practical settings.