Aggressive Dimensionality Reduction with Reinforcement Local Feature Selection for Text Categorization

A major problem of text categorization is the high dimensionality of the input feature space. This paper proposes a novel approach for aggressive dimensionality reduction in text categorization. This method utilizes the local feature selection to obtain more positive terms and then scales the weighting in the global level to suit the classifier. After that the weighting is enhanced with the feature selection measure to improve the distinguishing capability. The validity of this method is tested on two benchmark corpuses by the SVM classifier with four standard feature selection measures.