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A Composite Approach to Inducing Knowledge for Expert Systems Design

Author

Listed:
  • Ting-Peng Liang

    (Krannert Graduate School of Management, Purdue University, Krannert Building, West Lafayette, Indiana 47907)

Abstract

Knowledge acquisition is a bottleneck for expert system design. One way to overcome this bottleneck is to induce expert system rules from sample data. This paper presents a new induction approach called CRIS. The key notion employed in CRIS is that nominal and nonnominal attributes have different characteristics and hence should be analyzed differently. In the beginning of the paper, the benefits of this approach are described. Next, the basic elements of the CRIS approach are discussed and illustrated. This is followed by a series of empirical comparisons of the predictive validity of CRIS versus two entropy-based induction methods (ACLS and PLS1), statistical discriminant analysis, and the backpropagation method in neural networks. These comparisons all indicate that CRIS has higher predictive validity. The implications of the findings for expert systems design are discussed in the conclusion of the paper.

Suggested Citation

  • Ting-Peng Liang, 1992. "A Composite Approach to Inducing Knowledge for Expert Systems Design," Management Science, INFORMS, vol. 38(1), pages 1-17, January.
    • Handle: RePEc:inm:ormnsc:v:38:y:1992:i:1:p:1-17
    DOI: 10.1287/mnsc.38.1.1
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    Citations

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    Cited by:

    1. Kattan, MW & Cooper, RB, 1998. "The predictive accuracy of computer-based classification decision techniques.A review and research directions," Omega, Elsevier, vol. 26(4), pages 467-482, August.
    2. Srinivasan Ragothaman & Bijayananda Naik, 1994. "Using Rule Induction for Expert System Development: The Case of Asset Writedowns," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 3(3), pages 187-203, August.
    3. 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.
    4. 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.
    5. R. Slowinski & C. Zopounidis, 1995. "Application of the Rough Set Approach to Evaluation of Bankruptcy Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(1), pages 27-41, March.
    6. Srinivasan Ragothaman & Bijayananda Naik & Kumoli Ramakrishnan, 2003. "Predicting Corporate Acquisitions: An Application of Uncertain Reasoning Using Rule Induction," Information Systems Frontiers, Springer, vol. 5(4), pages 401-412, December.
    7. 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.
    8. Vijay S. Mookerjee & Michael V. Mannino, 2000. "Mean-Risk Trade-Offs in Inductive Expert Systems," Information Systems Research, INFORMS, vol. 11(2), pages 137-158, June.
    9. Deng, Pi-Sheng, 1996. "Using case-based reasoning approach to the support of ill-structured decisions," European Journal of Operational Research, Elsevier, vol. 93(3), pages 511-521, September.

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