Determining the saliency of input variables in neural network classifiers

Determining the saliency of input variables in neural network classifiers

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Article ID: iaor1998901
Country: United Kingdom
Volume: 24
Issue: 8
Start Page Number: 767
End Page Number: 773
Publication Date: Aug 1997
Journal: Computers and Operations Research
Authors: , ,
Keywords: statistics: general
Abstract:

This paper examines a measure of the saliency of the input variables that is based upon the connection weights of the neural network. Using Monte Carlo simulation techniques, a comparison of this method with the traditional stepwise variable selection rule in Fisher's linear classification analysis (FLDA) is made. It is found that the method works quite well in identifying significant variables under a variety of experimental conditions, including neural network architectures and data configurations. In addition, data from acquired and liquidated firms are used to illustrate and validate the technique.

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