Skip to Main Content
Feature selection is the process of selecting effective subsets of features that are effective in performing a given task. We propose an approach using a penalty function combined with a neural network to select a subset from a collection of features while maintaining the performance possible with the larger set. The penalty function is related to a mixed-norm function that has proven successful in pruning neural networks. The new function is shown to work on test cases with known redundancy and to be effective in feature selection for practical problems.