Abstract The Short Message Service (SMS) have an important economic impact for end
users and service providers. Spam is a serious universal problem that causes problems for …

This paper evaluates five supervised learning methods in the context of statistical spam
filtering. We study the impact of different feature pruning methods and feature set sizes on …

The growth of email users has resulted in the dramatic increasing of the spam emails.
Helpfully, there are different approaches able to automatically detect and remove most of …

The increasing volumes of unsolicited bulk e-mail (also known as spam) are bringing more
annoyance for most Internet users. Using a classifier based on a specific machine-learning …

We describe experiments with a Naive Bayes text classifier in the context of anti-spam E-
mail filtering, using two different statistical event models: a multi-variate Bernoulli model and …

In this paper, we propose a mechanism for filtering incoming spam mails by generating
spam mail prototypes using genetic algorithm. Firstly, words from e-mails are extracted and …

In the past few years, machine learning and in particular simple Naive Bayes classifiers
have proven their value in filtering spam emails. We hereby put Naive Bayes filters to the …

Over the last few decades, the advent of telecommunication systems has allowed a growing
exchange of electronic messages around the world. Unfortunately, irrelevant and/or …

Earlier methods on spam filtering usually compare the contents of emails against specific
keywords, which are not robust as the spammers frequently change the terms used in …