Olex Effective Rule Learning for Text Categorization – java /dotnet

Abstract
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 T_{1} or cdots or T_{n} occurs in document d, and none of T_{n + 1}, ldots T_{n + m} occurs in d, then classify d under category c,” where each T_{i} is 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