Automatic generation of text categorization rules in a hybrid method based on machine learning

This paper discusses several techniques for the automatic generation of rules to be used in a novel hybrid method for text categorization. This approach combines a machine learning algorithm along with a different rule-based expert systems in cascade used to filter and re-rank the output of the base model provided by the previous classifier. This paper describes an implementation based on kNN algorithm and a basic rule language that expresses lists of terms appearing in the text. The popular Reuters-21578 news corpus is used for testing. Results show that the proposed methods for automatic rule generation achieve precision values that are very similar to the ones achieved by manually defined rule sets, and that this hybrid approach achieves a precision that is comparable to other top state-of-the-art methods.