Concept drift in e-mail datasets: An empirical study with practical implications
Abstract Internet e-mail service emerged in the late seventies to implement fast message exchanging through computer networks. Network users immediately discovered the value of this service (sometimes for improper purposes such as spamming). As e-mail became indispensable to increase personal productivity, the volume of spam deliveries was constantly growing. With the passage of time, a great number of proposals and tools have emerged to fight against spam. However, the vast majority of them do not properly take into consideration the inner attributes of spam and ham messages such as the noise or the presence of concept drift. In this work, we provide a detailed empirical study of concept drift in the e-mail domain taking into consideration two key aspects: existing types of concept drift and the real class of messages (spam and ham). As a result, our study reveals different weaknesses of multiple e-mail filtering alternatives and other relevant works in this domain and identifies new strategies to develop more accurate filters. Finally, the experimentation carried out in this work has motivated the development of a concept drift analyser tool for the e-mail domain that can be freely downloaded from https://github.com/sing-group/conceptDriftAnalyser.git. Highlights Analysing concept drift for improving the accuracy of anti-spam filters. Spam defined as a set of messages whose topics are not interesting to a given user. Topic extraction, summarization and analysis of messages. Development of new evaluation strategies to tackle concept drift. Identification of inefficiencies of actual evaluation methods in presence of concept drift.
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