Combining Local Feature Scoring Methods for Text Categorization

Dimensionality reduction is an important process in text categorization. Feature scoring methods are used in order to realize this reduction. Features are evaluated and selection is performed according to a certain threshold. In this paper, we propose combining pairs of high-performing feature scoring methods to enhance text categorization. We analyzed the performance of constructing this combining by using three operators; the union operator (UN), the union-cut operator (UC), along with the intersection operator (INT) in order to increase the confidence in the selected features. The results suggested that these combining operators, when applied on feature selection methods with comparable performance achieves an improvement. Generally, the UC operator demonstrated the best enhanced performance in classifying frequent categories whereas the UN operator was effective in the classification of rare categories. Additionally, the INT operator showed some potential in terms of storage reduction and performace improvement.