RLS-MARS: An Effective Feature Selection Tool for Text Classification

The RLS-MARS (Regularized Least Squares-Multi Angle Regression and Shrinkage) feature selection model is used to select the relevant information, in which both, the keeping and the leaving-out of the regularizer are present. The RLS-MARS model is to find a series of directions in multidimensional space, leading the gradient vectors to change along those directions which would make the gradient matrix´s gradient descent, during the procedure, the feature in this direction can be easily selected. TF-IDCFC (Term Frequency Inverse Document and Category Frequency Collection normalization) weighting method is proposed to measure the features, by using category information as a factor. Our experiments on 20Newsgroups and Reuters-21578, all of those results demonstrate the effectiveness of the new feature selection method for text classification.