An efficient feature selection using multi-criteria in text categorization

Text categorization is a problem of assigning a document into one or more predefined classes. One of the most interesting issues in text categorization is feature selection. This paper proposes a novel approach in feature selection based on multicriteria ranking of features. Based on a threshold value for each criterion, a new procedure for feature selection is proposed and applied to a text categorization. Experiments dealing with the Reuters-21578 benchmark data and the naive Bayes algorithm show that the proposed approach outperforms performances in compare to conventional feature selection methods.