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Data mining using a genetic algorithm‐trained neural network

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  • Randall S. Sexton
  • Naheel A. Sikander

Abstract

Neural networks have been shown to perform well for mapping unknown functions from historical data in many business areas, such as accounting, finance, and management. Although there have been many successful applications of neural networks in business, additional information about the networks is still lacking, specifically, determination of inputs that are relevant to the neural network model. It is apparent that by knowing which inputs are actually contributing to model prediction a researcher has gained additional knowledge about the problem itself. This can lead to a parsimonious neural network architecture, better generalization for out‐of‐sample prediction, and, probably the most important, a better understanding of the problem. It is shown in this paper that by using a modified genetic algorithm for neural network training, relevant inputs can be determined while simultaneously searching for a global solution. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • Randall S. Sexton & Naheel A. Sikander, 2001. "Data mining using a genetic algorithm‐trained neural network," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(4), pages 201-210, December.
    • Handle: RePEc:wly:isacfm:v:10:y:2001:i:4:p:201-210
    DOI: 10.1002/isaf.205
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    References listed on IDEAS

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    1. Masson, Egill & Wang, Yih-Jeou, 1990. "Introduction to computation and learning in artificial neural networks," European Journal of Operational Research, Elsevier, vol. 47(1), pages 1-28, July.
    2. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
    3. Shouhong Wang, 1995. "The Unpredictability of Standard Back Propagation Neural Networks in Classification Applications," Management Science, INFORMS, vol. 41(3), pages 555-559, March.
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    Cited by:

    1. Adrian Costea & Iulian Nastac, 2005. "Assessing the predictive performance of artifIcial neural network‐based classifiers based on different data preprocessing methods, distributions and training mechanisms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(4), pages 217-250, December.

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