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Inductive Expert System Design: Maximizing System Value

Author

Listed:
  • Vijay S. Mookerjee

    (School of Business, University of Washington, Seattle, Washington 98195)

  • Brian L. Dos Santos

    (College of Business and Public Administration, University of Louisville, Louisville, Kentucky 40292-0001)

Abstract

There is a growing interest in the use of induction to develop a special class of expert systems known as inductive expert systems . Existing approaches to develop inductive expert systems do not attempt to maximize system value and may therefore be of limited use to firms. We present an induction algorithm that seeks to develop inductive expert systems that maximize value. The task of developing an inductive expert system is looked upon as one of developing an optimal sequential information acquisition strategy. Information is acquired to reduce uncertainty only if the benefits gained from acquiring the information exceed its cost. Existing approaches ignore the costs and benefits of acquiring information. We compare the systems developed by our algorithm with those developed by the popular ID3 algorithm. In addition, we present results from an extensive set of experiments that indicate that our algorithm will result in more valuable systems than the ID3 algorithm and the ID3 algorithm with pessimistic pruning.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orisre:v:4:y:1993:i:2:p:111-140
    DOI: 10.1287/isre.4.2.111
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    Citations

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    Cited by:

    1. Wolfgang Kerber & Jürgen-Peter Kretschmer & Georg von Wangenheim, 2008. "Optimal Sequential Investigation Rules in Competition Law," MAGKS Papers on Economics 200816, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Fang, Xiao & Rachamadugu, Ram, 2009. "Policies for knowledge refreshing in databases," Omega, Elsevier, vol. 37(1), pages 16-28, February.
    3. Zhiqiang Zheng & Balaji Padmanabhan, 2006. "Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution," Management Science, INFORMS, vol. 52(5), pages 697-712, May.
    4. 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.
    5. 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.
    6. Alain Bensoussan & Radha Mookerjee & Vijay Mookerjee & Wei T. Yue, 2009. "Maintaining Diagnostic Knowledge-Based Systems: A Control-Theoretic Approach," Management Science, INFORMS, vol. 55(2), pages 294-310, February.
    7. Voorberg, S. & van Jaarsveld, W. & Eshuis, R. & van Houtum, G.J., 2023. "Information acquisition for service contract quotations made by repair shops," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1166-1177.
    8. DeCroix, Gregory A. & Mookerjee, Vijay S., 1997. "Purchasing demand information in a stochastic-demand inventory system," European Journal of Operational Research, Elsevier, vol. 102(1), pages 36-57, October.
    9. 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.
    10. Dan J. Kim & Yujong Hwang, 2012. "A study of mobile internet user’s service quality perceptions from a user’s utilitarian and hedonic value tendency perspectives," Information Systems Frontiers, Springer, vol. 14(2), pages 409-421, April.
    11. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
    12. 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.

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