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Using modular neural networks for business decisions

Using modular neural networks for business decisions Understanding large amounts of information and efficiently using that information in improved decision making has become increasingly challenging as businesses collect terabytes of data. Businesses have turned to emerging technology including neural networks, symbolic learning, and genetic algorithms. In the current study, four classification methods were compared using results from an Indonesian contraceptive‐method preference survey. The four methods are linear discriminant analysis, quadratic discriminant analysis, backpropagation neural networks, and modular neural networks. The modular neural network is a more complex and less frequently used neural network model. This comparative study gives insight into its performance on classifying observations from a challenging data set, the 1987 National Indonesia Contraceptive Prevalence Survey. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management Decision Emerald Publishing

Using modular neural networks for business decisions

Management Decision , Volume 40 (1): 6 – Feb 1, 2002

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Publisher
Emerald Publishing
Copyright
Copyright © 2002 MCB UP Ltd. All rights reserved.
ISSN
0025-1747
DOI
10.1108/00251740210413361
Publisher site
See Article on Publisher Site

Abstract

Understanding large amounts of information and efficiently using that information in improved decision making has become increasingly challenging as businesses collect terabytes of data. Businesses have turned to emerging technology including neural networks, symbolic learning, and genetic algorithms. In the current study, four classification methods were compared using results from an Indonesian contraceptive‐method preference survey. The four methods are linear discriminant analysis, quadratic discriminant analysis, backpropagation neural networks, and modular neural networks. The modular neural network is a more complex and less frequently used neural network model. This comparative study gives insight into its performance on classifying observations from a challenging data set, the 1987 National Indonesia Contraceptive Prevalence Survey.

Journal

Management DecisionEmerald Publishing

Published: Feb 1, 2002

Keywords: Neural networks; Classification; Decision‐support systems

References

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