Using modular neural networks for business decisions

David Mitchell (University of Houston Downtown, Houston, Texas, USA)
Robert Pavur (University of North Texas, Denton, Texas, USA)

Management Decision

ISSN: 0025-1747

Publication date: 1 February 2002


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.



Mitchell, D. and Pavur, R. (2002), "Using modular neural networks for business decisions", Management Decision, Vol. 40 No. 1, pp. 58-63.

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Copyright © 2002, MCB UP Limited

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