Variable selection with false discovery rate control in deep neural networks
Authors:
- 29 March 2021
- journal article
- research article
- Published by Springer Nature in Nature Machine Intelligence
- Vol. 3 (5) , 426-433
Abstract
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Funding Information
- U.S. Department of Health & Human Services | National Institutes of Health (R01GM120733)
- National Science Foundation (1925645)
This publication has 31 references indexed in Scilit:
- Combined input variable selection and model complexity control for nonlinear regressionPattern Recognition Letters, 2009
- Non-linear variable selection for artificial neural networks using partial mutual informationEnvironmental Modelling & Software, 2008
- A hybrid approach for feature subset selection using neural networks and ant colony optimizationExpert Systems with Applications, 2007
- Statistical significance for genomewide studiesProceedings of the National Academy of Sciences, 2003
- The control of the false discovery rate in multiple testing under dependencyThe Annals of Statistics, 2001
- Neural networks for classification: a surveyIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2000
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998
- Neural Network Studies. 2. Variable SelectionJournal of Chemical Information and Computer Sciences, 1996
- Using mutual information for selecting features in supervised neural net learningIEEE Transactions on Neural Networks, 1994
- A simple procedure for pruning back-propagation trained neural networksIEEE Transactions on Neural Networks, 1990