Filtron: A Learning-Based Anti-Spam Filter

Abstract

We present Filtron, a prototype anti-spam filter that integrates the main empirical conclusions of our comprehensive analysis on using machine learning to construct e#ective personalized anti-spam filters. Filtron is based on the experimental results over several design parameters on four publicly available benchmark corpora. After describing Filtron's architecture, we assess its behavior in real use over a period of seven months. The results are deemed satisfactory, though they can be improved with more elaborate preprocessing and regular re-training