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 is used to illustrate and validate the technique.
| Original language | English |
|---|---|
| Pages (from-to) | 767-773 |
| Number of pages | 7 |
| Journal | Computers and Operations Research |
| Volume | 24 |
| Issue number | 8 |
| State | Published - Aug 1997 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Information Systems and Management
- Management Science and Operations Research
- Applied Mathematics
- Modeling and Simulation
- Transportation
Cite this
Determining the saliency of input variables in neural network classifiers. / Nath, Ravinder; Rajagopalan, Balaji; Ryker, Randy.
In: Computers and Operations Research, Vol. 24, No. 8, 08.1997, p. 767-773.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Determining the saliency of input variables in neural network classifiers
AU - Nath, Ravinder
AU - Rajagopalan, Balaji
AU - Ryker, Randy
PY - 1997/8
Y1 - 1997/8
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=0031209593&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0031209593
VL - 24
SP - 767
EP - 773
JO - Surveys in Operations Research and Management Science
JF - Surveys in Operations Research and Management Science
SN - 0305-0548
IS - 8
ER -