A Model for Fuzzy Logic Based Machine Learning Approach for Spam Filtering
Mehdi Samiei yeganeh · IOSR Journal of Computer Engineering · 2012
It is definitely impossible to say, who was that first person to come up with the simple idea of sending out a public announcement to millions of people, and then at least one person will react to it no matter what is the proposal.E-mail provides a perfect way to send these millions of advertisements without any for a sender, and this fortunate fact is nowadays extensively exploited by several organizations.As a result, the e-mail boxes of millions of people get cluttered with all these so-called Unsolicited Bulk E-mail (UBE) also known as "spam" or "junk mail".E-mail spam, is a subset of electronic spam involving nearly identical messages sent to numerous recipients through e-mail.Definitions of spam usually include the aspects that e-mail is unsolicited and sent in bulk.Another subset of UBE is UCE (Unsolicited Commercial E-mail).The opposite of "spam", email which one wants, is called "ham", usually when referring to a message's automated analysis (such as Bayesian filtering).Machine learning techniques now days are used to automatically filter the spam e-mail in a very successful and efficient way.In this paper we consider some of the machine learning methods such as Naïve Bayes, Artificial Neural Networks, Artificial Immune System Classifier methods, and fuzzy logic.