Title :
RLS-MARS: An Effective Feature Selection Tool for Text Classification
Author :
Li Xi ; Dai Hang ; Wang Mingwen
Author_Institution :
Sch. of Math. & Comput. Sci., Jiangxi Sci. & Technol. Normal Univ., Nanchang, China
Abstract :
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.
Keywords :
feature extraction; gradient methods; matrix algebra; pattern classification; regression analysis; text analysis; vectors; RLS-MARS feature selection model; TF-IDCFC weighting method; category information; feature measurement; free text documents; gradient descent; gradient matrix; gradient vectors; multidimensional space; regularized least squares-multiangle regression-and-shrinkage; term frequency inverse document-and-category frequency collection normalization; text classification; Computational modeling; Educational institutions; Feature extraction; Semantics; Support vector machines; Text categorization; Vectors; Feature Selection; RLS-MARS; TF-IDCFC; Text Classification;
Conference_Titel :
Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-3093-0
DOI :
10.1109/MINES.2012.194
