An Evaluation on the Efficiency of Hybrid Feature Selection in Spam Email Classification

Abstract

Spam has serious negative on the usability of email and network resources. Spam is flooding the internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. And despite the evolution
of anti spam software, such as spam filters and spam blockers, the negative effects of spam are still being felt by individuals and businesses alike. To prevent this advance techniques are necessary. Our proposed method divides e-mails in spam class and non spam class according to different attribute values of spam. an alternative approach using a neural network (NN) classifier brained on a corpus of e-mail messages from several users. The features selection used in this work is one of the major improvements.