SMS Spam Detection Using TFIDF and Voting Classifier
Ganesh Ubale, Siddharth Gaikwad
Abstract
In today’s digital world, Mobile SMS (short message service) communication has almost become a part of every human life. Meanwhile each mobile user suffers from the harass of Spam SMS. These Spam SMS constitute veritable nuisance to mobile subscribers. Though hackers or spammers try to intrude in mobile computing devices, SMS support for mobile devices become more vulnerable as attacker tries to intrude into the system by sending unsolicited messages. An attacker can gain remote access over mobile devices. We propose a novel approach that can analyze message content and find features using the TF-IDF techniques to efficiently detect Spam Messages and Ham messages using different Machine Learning Classifiers. The Classifiers going to use in proposed work can be measured with the help of metrics such as Accuracy, Precision and Recall. In our proposed approach accuracy rate will be increased by using the Voting Classifier.