Learning Rules that Classify E-mail

Machine Learning in Information Access

Volume

Papers from the 1996 AAAI Spring Symposium

Contents

Two methods for learning text classifiers are compared on classification problems that might arise in filtering and filing personM e-mail messages: a "traxiitionM IR" method based on TF-IDF weighting, and a new method for learning sets of "keyword-spotting rules" based on the RIPPER rule learning algorithm. It is demonstrated that both methods obtain significant generalizations from a small number of examples; that both methods are comparable in generalization performance on problems of this type; and that both methods axe reasonably efficient, even with fairly large training sets. However, the greater comprehensibility of the rules may be advantageous in a system that allows users to extend or otherwise modify a learned classifier.