IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v136y2002i3p603-615.html
   My bibliography  Save this article

A comparative study of the effect of the position of outliers on classical and nontraditional approaches to the two-group classification problem

Author

Listed:
  • Pavur, Robert

Abstract

No abstract is available for this item.

Suggested Citation

  • Pavur, Robert, 2002. "A comparative study of the effect of the position of outliers on classical and nontraditional approaches to the two-group classification problem," European Journal of Operational Research, Elsevier, vol. 136(3), pages 603-615, February.
    • Handle: RePEc:eee:ejores:v:136:y:2002:i:3:p:603-615
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(01)00069-8
    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. 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. Hosseini, Jamshid C. & Armacost, Robert L., 1994. "The two-group discriminant problem with equal group mean vectors: An experimental evaluation of six linear/nonlinear programming formulations," European Journal of Operational Research, Elsevier, vol. 77(2), pages 241-252, September.
    3. Carol Markowski & Edward Markowski, 1997. "Evaluation of an adaptive discriminant procedure," Annals of Operations Research, Springer, vol. 74(0), pages 211-222, November.
    4. Stam, Antonie & Joachimsthaler, Erich A., 1990. "A comparison of a robust mixed-integer approach to existing methods for establishing classification rules for the discriminant problem," European Journal of Operational Research, Elsevier, vol. 46(1), pages 113-122, May.
    5. Lewis, Robert P. & Taha, Hamdy A., 1995. "An investigation of the use of goal programming to fit response surfaces," European Journal of Operational Research, Elsevier, vol. 86(3), pages 537-548, November.
    6. Lam, Kim Fung & Choo, Eng Ung & Moy, Jane W., 1996. "Minimizing deviations from the group mean: A new linear programming approach for the two-group classification problem," European Journal of Operational Research, Elsevier, vol. 88(2), pages 358-367, January.
    7. Ostermark, Ralf & Hoglund, Rune, 1998. "Addressing the multigroup discriminant problem using multivariate statistics and mathematical programming," European Journal of Operational Research, Elsevier, vol. 108(1), pages 224-237, July.
    8. Bill L. Seaver & Konstantinos P. Triantis, 1995. "The Impact of Outliers and Leverage Points for Technical Efficiency Measurement Using High Breakdown Procedures," Management Science, INFORMS, vol. 41(6), pages 937-956, June.
    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. J J Glen, 2008. "An additive utility mixed integer programming model for nonlinear discriminant analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1492-1505, November.
    3. Glen, J.J., 2006. "A comparison of standard and two-stage mathematical programming discriminant analysis methods," European Journal of Operational Research, Elsevier, vol. 171(2), pages 496-515, June.
    4. J. J. Glen, 2004. "Dichotomous categorical variable formation in mathematical programming discriminant analysis models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 575-596, June.

    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. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
    2. Lam, Kim Fung & Moy, Jane W., 2002. "Combining discriminant methods in solving classification problems in two-group discriminant analysis," European Journal of Operational Research, Elsevier, vol. 138(2), pages 294-301, April.
    3. Loucopoulos, Constantine, 2001. "Three-group classification with unequal misclassification costs: a mathematical programming approach," Omega, Elsevier, vol. 29(3), pages 291-297, June.
    4. Burcu Dikmen & Güray Küçükkocaoğlu, 2010. "The detection of earnings manipulation: the three-phase cutting plane algorithm using mathematical programming," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 442-466.
    5. 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.
    6. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    7. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    8. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    9. 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.
    10. repec:hum:wpaper:sfb649dp2013-037 is not listed on IDEAS
    11. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
    12. 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.
    13. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    14. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    15. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    16. Kattan, Michael W. & Cooper, Randolph B., 2000. "A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions," Omega, Elsevier, vol. 28(5), pages 501-512, October.
    17. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
    18. Antonie Stam & Cliff T. Ragsdale, 1992. "On the classification gap in mathematical programming‐based approaches to the discriminant problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(4), pages 545-559, June.
    19. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    20. Greta Falavigna, 2008. "Nouveaux instruments d’évaluation pour le risque financier d’entreprise," CERIS Working Paper 200801, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    21. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.

    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:136:y:2002:i:3:p:603-615. 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.