learning rules with negation for text categorization
This paper describes Olex, a novel method for the automatic construction of rule-based text classifiers. Olex relies on an optimization algorithm whereby a set of (both positive and negative) discriminating terms is generated for the category being learned. Such terms are then used to construct a classifier of the form "if term t 1 or ... term t n occurs in document d , and none of terms t n --1 ,    t n--m occurs in d , then d belongs to category c ". The proposed method is simple and elegant. Despite this, the results of a systematic experimentation performed on both the REUTERS-21578 and the OHSUMED data collections show that Olex is both effective and efficient.
negative features text classification classification rules