A Comprehensive Filter Feature Selection for Improving Document Classification

High dimension of bag-of-words vectors poses a serious challenge from sparse data, overfitting, irrelevant features to document classification. Filter feature selection is one of effective methods for dimensionality reduction by removing irrelevant features from feature set. This paper focuses on two main problems of filter feature selection which are the feature score computation and the imbalance in the feature selection performance between categories. We propose a novel filter feature selection method, named ExFCFS, to comprehensively resolve these problems. We experiment on related filter feature selection methods with two benchmark datasets Reuters-21578 dataset and Ohsumed dataset. The experimental results show the effectiveness of our solutions in terms of both Micro-F1 measure and Macro-F1 measure. Keywords— bag-of-words vector, filter feature selection, document classification