Feature Extraction and Classification of Spam Emails

Emails are a popular and preferred way of written communication in our daily life. The problem with emails is spam. These spam emails are sent with different intentions, but advertisement and fraud are the main reasons. As being inexpensive to send, it causes many problems to the internet society. This paper discusses the use of different feature extraction methods coupled with two different supervised machine learning classifiers evaluated using four performance metrics on two publicly available spam email datasets for spam filtering. We highlight the importance of the correct coupling of feature extraction and classifier, and the merits of using two independent datasets.