Class dependent feature scaling method using naive Bayes classifier for text datamining

The problem of feature selection is to find a subset of features for optimal classification. A critical part of feature selection is to rank features according to their importance for classification. The naive Bayes classifier has been extensively used in text categorization. We have developed a new feature scaling method, called class–dependent–feature–weighting (CDFW) using naive Bayes (NB) classifier. A new feature scaling method, CDFW–NB–RFE, combines CDFW and recursive feature elimination (RFE). Our experimental results showed that CDFW–NB–RFE outperformed other popular feature ranking schemes used on text datasets.