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Mean-Risk Trade-Offs in Inductive Expert Systems

Author

Listed:
  • Vijay S. Mookerjee

    (Department of Management Science, Box 353200, University of Washington, Seattle, Washington 98195)

  • Michael V. Mannino

    (Graduate School of Business Administration, Campus Box 165, P. O. Box 173364, University of Colorado at Denver, Denver, Colorado 80217)

Abstract

Notably absent in previous research on inductive expert systems is the study of meanrisk trade-offs. Such trade-offs may be significant when there are asymmetries such as unequal classification costs, and uncertainties in classification and information acquisition costs. The objective of this research is to developmodels to evaluate mean-risk trade-offs in value-based inductive approaches. We develop a combined mean-risk measure and incorporate it into the Risk-Based induction algorithm. The mean-risk measure has desirable theoretical properties (consistency and separability) and is supported by empirical results on decision making under risk. Simulation results using the Risk-Based algorithm demonstrate: (i) an order of magnitude performance difference between mean-based and risk-based algorithms and (ii) an increase in the performance difference between these algorithms as either risk aversion, uncertainty, or asymmetry increases given modest thresholds of the other two factors.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orisre:v:11:y:2000:i:2:p:137-158
    DOI: 10.1287/isre.11.2.137.11777
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    References listed on IDEAS

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    Cited by:

    1. 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.

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