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Neural network pruning and simultaneous feature and structure selection
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DOI：10.1016/j.neunet.2026.108977.png)
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Abstract
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Neural network pruning makes large neural networks more portable and smaller neural networks more interpretable with minimal loss of predictive accuracy. We propose a pruning method that reformulates the neural-network LASSO problem as a standard weighted regression or classification problem with a LASSO penalty. We apply this method starting from a dense neural network structure that includes all possible feed-forward networks as subnetworks. This efficiently removes substantial redundancy. To further refine the network, we develop a second step, which cycles over all remaining links, one at a time, removing those that do not sufficiently improve the model fit.
Keywords:
Neural network
Feature selection
Model selection
LASSO
Neural network architecture
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