Adaptive ant colony decision forest in automatic categorization of emails

Abstract

In this article an approach to the automatic classification of email messages in mailboxes has been proposed. The aim of this paper is to devise methods to build decision tables from the collection of email messages on which it is possible to build Ant Colony Optimization-based ensemble classifiers, whose application allows to use the collection of emails without cleaning, at the same time improving the accuracy of the email folders classification. The proposed method has been tested by the selected algorithms on the Enron Email Dataset. The results confirm that the proposed solutions allows to improve the accuracy of classification of new emails to folders.