Junk e-mail detection and filtering can be considered a cost-sensitive classification problem.
Nevertheless, preprocessing methods and noise reduction strategies used to enhance the …

It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited
bulk e-mail (" spam"). We conduct a thorough evaluation of this proposal on a corpus that we …

Spam is a universal problem with which everyone is familiar. A number of approaches are
used for Spam filtering. The most common filtering technique is content-based filtering which …

Using machine learning techniques such as naive Bayes, decision trees and support vector
machines to automatically filter out spam e-mails has drawn many researchers' attention …

During recent years, usage of social media has increased enormously. Billions of users use
Twitter, Youtube etc which has resulted in the increase in spams as well. Spammers use …

Spam represents a genuine irritation for email users, since it often disturbs them during their
work or free time. Machine learning approaches are commonly utilized as the engine of …

This paper presents a content-based approach to spam detection based on low-level
information. Instead of the traditional'bag of words' representation, we use a'bag of character …

The significant increase in the volume of fake (spam) messages has led to an urgent need to
develop and implement a robust anti‐spam method. Several of the current anti‐spam …

The growing volume of spam Emails has generated the need for a more precise anti-spam
filter to detect unsolicited Emails. One of the most common representations used in spam …

Background: As people using social media increases the data generation also increases
and the data generated may be safe or unsafe. If we see some applications like Twitter and …