SMS Spam Filtering Using Supervised Machine Learning Algorithms

This paper presents detection of Spam and ham messages using various supervised machine learning algorithms like naive Bayes Algorithm, support vector machines algorithm, and the maximum entropy algorithm and compares their performance in filtering the Ham and Spam messages. As people indulge more in Web-based activities, and with rising sharing of private-data by companies, SMS spam is very common. SMS spam filter inherits much functionality from E-mail Spam Filtering. Comparing the performance of various supervised learning algorithms we find the support vector machine algorithm gives us the most accurate result.