5 juil. 2015 Villeneuve d'Ascq (Lille) (France)
Sparsification of Linear Models for Large-Scale Text Classification
Simon Moura  1@  , Ioannis Partalas  2, *@  , Massih-Reza Amini  1, *@  
1 : Laboratoire d'Informatique de Grenoble  (LIG)  -  Site web
CNRS : UMR5217, Université Pierre-Mendès-France - Grenoble II, Institut polytechnique de Grenoble (Grenoble INP), Université Joseph Fourier - Grenoble I
UMR 5217 - Laboratoire LIG - 38041 Grenoble cedex 9 - France Tél. : +33 (0)4 76 51 43 61 - Fax : +33 (0)4 76 51 49 85 -  France
2 : VISEO - Objet Direct  -  Site web
VISEO - Objet Direct
Le Pulsar 4 avenue du Doyen Louis Weil 38000 GRENOBLE -  France
* : Auteur correspondant

In this paper we propose a simple yet effective method for sparsifying a posteriori linear models for large-scale text classification. The objective is to maintain high performance while reducing the prediction time by producing very sparse models. This is especially important in real-case scenarios where one deploys predictive models in several machines across the network and constraints apply on the prediction time.
We empirically evaluate the proposed approach in a large collection of documents from the Large-Scale Hierarchical Text Classification Challenge. The comparison with a feature selection method and LASSO regularization shows that we achieve to obtain a sparse representation improving in the same time the classification performance.



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