Skip to Main content Skip to Navigation
New interface
Journal articles

Feature extraction with neural networks

Philippe Leray 1 Patrick Gallinari 1 
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : The observed features of a given phenomenon are not all equally informative : some may be noisy, others correlated or irrelevant. The purpose of feature selection is to select a set of features pertinent to a given task. This is a complex process, but it is an important issue in many fields. In neural networks, feature selection has been studied for the last ten years, using conventional and original methods. This paper is a review of neural network approaches to feature selection. We first briefly introduce baseline statistical methods used in regression and classification. We then describe families of methods which have been developed specifically for neural networks. Representative methods are then compared on different test problems.
Document type :
Journal articles
Complete list of metadata
Contributor : Lip6 Publications Connect in order to contact the contributor
Submitted on : Friday, August 14, 2015 - 3:52:41 PM
Last modification on : Wednesday, September 21, 2022 - 2:30:30 PM

Links full text



Philippe Leray, Patrick Gallinari. Feature extraction with neural networks. Behaviormetrika, 1999, 26 (1), pp.145-166. ⟨10.2333/bhmk.26.145⟩. ⟨hal-01184481⟩



Record views