ABSTRACT

Today email has become one the fastest and most effective form of communication. The popularity of this mode of transmitting goods, information and services has motivated spammers to perfect their technical skills to fool spam filters. This development has worsened the problems faced by Internet users as they have to deal with email congestion, email overload and unprioritised email messages. The result was an exponential increase in the number of email classification management tools for the past few decades. In this paper we propose a new spam classifier using a learning process of multilayer neural network to implement back propagation technique. Our contribution to the body of knowledge is the use of an improved empirical analysis to choose an optimum, novel collection of attributes of a user�s email contents that allows a quick detection of most important words in emails. We also demonstrate the effectiveness of two equal sets of emails training and testing data.

Title	:	Backpropagation Learning Algorithms for Email Classification.
Authors	:	David Ndumiyana and Tarirayi Mukabeta
Keywords	:	Back propagation algorithm, neural networks, machine learning, multilayer perceptron, false positives.
Issue Date	:	July 2016.
Abstract	:	div>
Page(s)	:	229-235
ISSN	:	0975–3397
Source	:	Vol. 8, Issue.07