Determining the saliency of input variables in neural network classifiers

Ravinder Nath, Balaji Rajagopalan, Randy Ryker

Research output: Contribution to journalArticlepeer-review

83 Scopus citations


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 is used to illustrate and validate the technique.

Original languageEnglish (US)
Pages (from-to)767-773
Number of pages7
JournalComputers and Operations Research
Issue number8
StatePublished - Aug 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research


Dive into the research topics of 'Determining the saliency of input variables in neural network classifiers'. Together they form a unique fingerprint.

Cite this