SMS SPAM DETECTOR USING MACHINE LEARNING

Over recent years, as the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollar industry. At the same time, a reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. The lack of real archive for SMS mailshots, a economize of messages, gallop features, and their demonic language are the factors that may cause the established email filtering algorithms to underperform in their classification. In this predict, a database of authentic SMS Spam from the UCI Machine Learning repository is used, and after pre-processing and feature extraction, different machine learning techniques are applied to the database. Finally, the results are compared and the best algorithm for spam filtering for text messaging is introduced. Final simulation solution using 10-fold cross-validation show the best unified in this work force into the overall error rate of the best model in the original paper citing this dataset by more than half. Algorithms used in this technique are: Logistic regression (LR), K-nearest neighbor (K-NN), and Decision tree (DT) are used for the classification of spam messages in mobile device communication. The SMS spam collection set is used for testing the method.