IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v138y2002i1p155-177.html
   My bibliography  Save this article

A computational study on the performance of artificial neural networks under changing structural design and data distribution

Author

Listed:
  • Pendharkar, Parag C.

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:138:y:2002:i:1:p:155-177
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(01)00085-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gary J. Koehler, 1991. "Linear Discriminant Functions Determined by Genetic Search," INFORMS Journal on Computing, INFORMS, vol. 3(4), pages 345-357, November.
    2. Hung, Ming S. & Denton, James W., 1993. "Training neural networks with the GRG2 nonlinear optimizer," European Journal of Operational Research, Elsevier, vol. 69(1), pages 83-91, August.
    3. Sexton, Randall S. & Alidaee, Bahram & Dorsey, Robert E. & Johnson, John D., 1998. "Global optimization for artificial neural networks: A tabu search application," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 570-584, April.
    4. Ting†Peng Liang & John S. Chandler & Ingoo Han & Jinsheng Roan, 1992. "An empirical investigation of some data effects on the classification accuracy of probit, ID3, and neural networks," Contemporary Accounting Research, John Wiley & Sons, vol. 9(1), pages 306-328, September.
    5. 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.
    6. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
    7. Akhil Kumar & Ignacio Olmeda, 1999. "A Study of Composite or Hybrid Classifiers for Knowledge Discovery," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 267-277, August.
    8. Curry, B. & Morgan, P., 1997. "Neural networks: a need for caution," Omega, Elsevier, vol. 25(1), pages 123-133, February.
    9. Abad, P. L. & Banks, W. J., 1993. "New LP based heuristics for the classification problem," European Journal of Operational Research, Elsevier, vol. 67(1), pages 88-100, May.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. de Andres, Javier & Landajo, Manuel & Lorca, Pedro, 2005. "Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case," European Journal of Operational Research, Elsevier, vol. 167(2), pages 518-542, December.
    2. P Pendharkar, 2009. "Misclassification cost minimizing fitness functions for genetic algorithm-based artificial neural network classifiers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1123-1134, August.
    3. Parag Pendharkar & Sudhir Nanda, 2006. "A misclassification cost‐minimizing evolutionary–neural classification approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 432-447, August.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Sudhir Nanda & Parag Pendharkar, 2001. "Linear models for minimizing misclassification costs in bankruptcy prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(3), pages 155-168, September.
    3. Parag Pendharkar & Sudhir Nanda, 2006. "A misclassification cost‐minimizing evolutionary–neural classification approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 432-447, August.
    4. Palocsay, Susan W. & Stevens, Scott P. & Brookshire, Robert G. & Sacco, William J. & Copes, Wayne S. & Buckman, Robert F. & Smith, J. Stanley, 1996. "Using neural networks for trauma outcome evaluation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 369-386, September.
    5. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
    6. 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.
    7. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
    8. Andreas Charitou & Chris Charalambous, 1996. "The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 5(4), pages 199-215, December.
    9. 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.
    10. Gupta, Jatinder N. D. & Sexton, Randall S., 1999. "Comparing backpropagation with a genetic algorithm for neural network training," Omega, Elsevier, vol. 27(6), pages 679-684, December.
    11. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    12. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
    13. 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.
    14. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    15. Palocsay, Susan W. & Wang, Ping & Brookshire, Robert G., 2000. "Predicting criminal recidivism using neural networks," Socio-Economic Planning Sciences, Elsevier, vol. 34(4), pages 271-284, December.
    16. Denton, James W. & Hung, Ming S., 1996. "A comparison of nonlinear optimization methods for supervised learning in multilayer feedforward neural networks," European Journal of Operational Research, Elsevier, vol. 93(2), pages 358-368, September.
    17. Barniv, Ran & Mehrez, Abraham & Kline, Douglas M., 2000. "Confidence intervals for controlling the probability of bankruptcy," Omega, Elsevier, vol. 28(5), pages 555-565, October.
    18. Murugan Anandarajan & Picheng Lee & Asokan Anandarajan, 2001. "Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 69-81, June.
    19. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "A tabu search algorithm for the training of neural networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 282-291, February.
    20. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:138:y:2002:i:1:p:155-177. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.