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Feature Selection with Neural Networks

Philippe Leray 1 Patrick Gallinari 1 
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Features gathered from the observation of a phenomenon are not all equally informative: some of them may be noisy, correlated or irrelevant. Feature selection aims at selecting a feature set that is relevant for a given task. This problem is complex and remains an important issue in many domains. In the field of neural networks, feature selection has been studied for the last ten years and classical as well as original methods have been employed. 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.
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Submitted on : Monday, April 20, 2020 - 11:21:50 AM
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  • HAL Id : hal-02547719, version 1


Philippe Leray, Patrick Gallinari. Feature Selection with Neural Networks. [Research Report] lip6.1998.012, LIP6. 1998. ⟨hal-02547719⟩



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