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Generating artificial data with monotonicity constraints

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  • Potharst, R.
  • van Wezel, M.C.

Abstract

The monotonicity constraint is a common side condition imposed on modeling problems as diverse as hedonic pricing, personnel selection and credit rating. Experience tells us that it is not trivial to generate artificial data for supervised learning problems when the monotonicity constraint holds. Two algorithms are presented in this paper for such learning problems. The first one can be used to generate random monotone data sets without an underlying model, and the second can be used to generate monotone decision tree models. If needed, noise can be added to the generated data. The second algorithm makes use of the first one. Both algorithms are illustrated with an example.

Suggested Citation

  • Potharst, R. & van Wezel, M.C., 2005. "Generating artificial data with monotonicity constraints," Econometric Institute Research Papers EI 2005-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1916
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    References listed on IDEAS

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    1. Bioch, J.C. & Popova, V., 2003. "Induction of Ordinal Decision Trees," ERIM Report Series Research in Management ERS-2003-008-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
    3. Bioch, J.C. & Popova, V., 2002. "Monotone Decision Trees and Noisy Data," ERIM Report Series Research in Management ERS-2002-53-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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