Olex: Effective Rule Learning for Text Categorization
Pasquale Rullo, Veronica L. Policicchio, Chiara Cumbo, Salvatore Iiritano · IEEE Transactions on Knowledge and Data Engineering · 2008
This paper describes Olex, a novel method for the automatic induction of rule-based text classifiers. Olex supports a hypothesis language of the form "if T1or hellip or Tnoccurs in document d, and none of T1+n,... Tn+moccurs in d, then classify d under category c," where each Tiis a conjunction of terms. The proposed method is simple and elegant. Despite this, the results of a systematic experimentation performed on the REUTERS-21578, the OHSUMED, and the ODP data collections show that Olex provides classifiers that are accurate, compact, and comprehensible. A comparative analysis conducted against some of the most well-known learning algorithms (namely, Naive Bayes, Ripper, C4.5, SVM, and Linear Logistic Regression) demonstrates that it is more than competitive in terms of both predictive accuracy and efficiency.