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A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions

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  • Kattan, Michael W.
  • Cooper, Randolph B.

Abstract

Machine learning techniques, such as neural networks and rule induction, are becoming popular alternatives to traditional statistical techniques for solving classification problems. However, much of the research has been devoted to comparing performances upon sample data sets, with little attention paid to why a technique sometimes outperforms another. This study describes a simulation, which examined the effects of factors with theoretical support for their differential impacts upon three machine learning techniques (a backpropagation neural network and two rule induction techniques: CART and ID3) and discriminant analysis. The results demonstrate significant differences in the techniques' abilities to reduce overfitting, to form diagonal partitions, and to compensate for variations between actual and sample data class proportions. This helps explain why a particular technique may perform well in one context and not in another.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jomega:v:28:y:2000:i:5:p:501-512
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    References listed on IDEAS

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    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. McClelland, John W. & Wetzstein, Michael E. & Musser, Wesley N., 1986. "Returns To Scale And Size In Agricultural Economics," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 11(2), pages 1-5, December.
    3. 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.
    4. Ting-Peng Liang, 1992. "A Composite Approach to Inducing Knowledge for Expert Systems Design," Management Science, INFORMS, vol. 38(1), pages 1-17, January.
    5. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    6. William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, December.
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    Cited by:

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