Incremental e-mail classification and rule suggestion using simple term statistics

Abstract

In this paper, we present and use a method for e-mail categorization based on simple term statistics updated incrementally. We apply simple term statistics to two different tasks. The first task is to predict folders for classification of e-mails when large numbers of messages are required to remain unclassified. The second task is to support users who define rule bases for the same classification task, by suggesting suitable keywords for constructing Ripple Down Rule bases in this scenario. For both tasks, the results are compared with a number of standard machine learning algorithms. The comparison shows that the simple term statistics method achieves a higher level of accuracy than other machine learning methods when taking computation time into account. © Springer-Verlag Berlin Heidelberg 2009.