Data-driven global-ranking local feature selection methods for text categorization
We propose two filtering methods for text categorization. The proposed methods find the number of features based on a low range parameter. The proposal ensures contribution of each document in the final subset of features. The proposed methods obtain better results than VR and ALOFT algorithms.
feature selection filtering method aloft text classification high dimensionality variable ranking