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A misclassification cost‐minimizing evolutionary–neural classification approach

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  • Parag Pendharkar
  • Sudhir Nanda

Abstract

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.

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  • 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.
    • Handle: RePEc:wly:navres:v:53:y:2006:i:5:p:432-447
    DOI: 10.1002/nav.20154
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    1. 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.
    2. Catherine K. Murphy & Michel Benaroch, 1997. "Adding Value to Induced Decision Trees for Time-Sensitive Data," INFORMS Journal on Computing, INFORMS, vol. 9(4), pages 385-396, November.
    3. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    4. Bhattacharyya, Siddhartha & Troutt, Marvin D., 2003. "Genetic search over probability spaces," European Journal of Operational Research, Elsevier, vol. 144(2), pages 333-347, January.
    5. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    6. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    7. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    8. Vijay S. Mookerjee & Michael V. Mannino, 1997. "Redesigning Case Retrieval to Reduce Information Acquisition Costs," Information Systems Research, INFORMS, vol. 8(1), pages 51-68, March.
    9. 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.
    10. 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.
    11. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    12. 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.
    13. 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.
    14. Michael V. Mannino & Vijay S. Mookerjee, 1999. "Optimizing Expert Systems: Heuristics for Efficiently Generating Low-Cost Information Acquisition Strategies," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 278-291, August.
    15. Vijay S. Mookerjee & Brian L. Dos Santos, 1993. "Inductive Expert System Design: Maximizing System Value," Information Systems Research, INFORMS, vol. 4(2), pages 111-140, June.
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    Cited by:

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    2. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..

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