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Using Feature Construction to Improve the Performance of Neural Networks

Author

Listed:
  • Selwyn Piramuthu

    (Decision and Information Sciences, University of Florida, Gainesville, Florida 32611-7169)

  • Harish Ragavan

    (Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801)

  • Michael J. Shaw

    (Department of Business Administration, University of Illinois at Urbana-Champaign, Champaign, Illinois 61820)

Abstract

Recent years have seen the growth in popularity of neural networks for business decision support because of their capabilities for modeling, estimating, and classifying. Compared to other AI methods for problem solving such as expert systems, neural network approaches are especially useful for their ability to learn adaptively from observations. However, neural network learning performed by algorithms such as back-propagation (BP) are known to be slow due to the size of the search space involved and also the iterative manner in which the algorithm works. In this paper, we show that the degree of difficulty in neural network learning is inherent in the given set of training examples. We propose a technique for measuring such learning difficulty, and then develop a feature construction methodology that helps transform the training data so that both the learning speed and classification accuracy of neural network algorithms are improved. We show the efficacy of the proposed method for financial risk classification, a domain characterized by frequent data noise, lack of functional structure, and high attribute interactions. Moreover, the empirical studies also provide insights into the structural characteristics of neural networks with respect to the input data used as well as possible mechanisms to improve the learning performance.

Suggested Citation

  • Selwyn Piramuthu & Harish Ragavan & Michael J. Shaw, 1998. "Using Feature Construction to Improve the Performance of Neural Networks," Management Science, INFORMS, vol. 44(3), pages 416-430, March.
  • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:3:p:416-430
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    File URL: http://dx.doi.org/10.1287/mnsc.44.3.416
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    References listed on IDEAS

    as
    1. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    2. Ang, James S & Patel, Kiritkumar A, 1975. "Bond Rating Methods: Comparison and Validation," Journal of Finance, American Finance Association, vol. 30(2), pages 631-640, May.
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    Citations

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    Cited by:

    1. TOBBACK, Ellen & MOEYERSOMS, Julie & STANKOVA, Marija & MARTENS, David, 2016. "Bankruptcy prediction for SMEs using relational data," Working Papers 2016004, University of Antwerp, Faculty of Applied Economics.
    2. repec:pal:jorsoc:v:57:y:2006:i:3:d:10.1057_palgrave.jors.2602006 is not listed on IDEAS
    3. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    4. Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével
      [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
    5. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    6. repec:eee:spacre:v:15:y:2012:i:1:p:7-58 is not listed on IDEAS
    7. Pendharkar, Parag C., 2002. "A computational study on the performance of artificial neural networks under changing structural design and data distribution," European Journal of Operational Research, Elsevier, vol. 138(1), pages 155-177, April.
    8. Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
    9. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    10. Pendharkar, Parag C., 2001. "An empirical study of design and testing of hybrid evolutionary-neural approach for classification," Omega, Elsevier, vol. 29(4), pages 361-374, August.
    11. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    12. repec:pal:jorsoc:v:54:y:2003:i:3:d:10.1057_palgrave.jors.2601523 is not listed on IDEAS

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