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Classification Trees for Problems with Monotonicity Constraints

Author

Listed:
  • Potharst, R.
  • Feelders, A.J.

Abstract

For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classification problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not produce monotone trees, even if the dataset is completely monotone. This paper surveys the methods that have so far been proposed for generating decision trees that satisfy monotonicity constraints. A distinction is made between methods that work only for monotone datasets and methods that work for monotone and non-monotone datasets alike.

Suggested Citation

  • Potharst, R. & Feelders, A.J., 2002. "Classification Trees for Problems with Monotonicity Constraints," ERIM Report Series Research in Management ERS-2002-45-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.
    • Handle: RePEc:ems:eureri:195
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    Citations

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    Cited by:

    1. S. Lievens & B. De Baets & K. Cao-Van, 2008. "A probabilistic framework for the design of instance-based supervised ranking algorithms in an ordinal setting," Annals of Operations Research, Springer, vol. 163(1), pages 115-142, October.
    2. Yang, Bill Huajian, 2019. "Monotonic Estimation for Probability Distribution and Multivariate Risk Scales by Constrained Minimum Generalized Cross-Entropy," MPRA Paper 93400, University Library of Munich, Germany.
    3. Tyler J. VanderWeele & James M. Robins, 2010. "Signed directed acyclic graphs for causal inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 111-127, January.
    4. Yunli Yang & Baiyu Chen & Zhouwang Yang, 2020. "An Algorithm for Ordinal Classification Based on Pairwise Comparison," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 158-179, April.
    5. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
    6. Yang, Bill Huajian, 2019. "Resolutions to flip-over credit risk and beyond," MPRA Paper 93389, University Library of Munich, Germany.
    7. Du, Wen Sheng & Hu, Bao Qing, 2018. "A fast heuristic attribute reduction approach to ordered decision systems," European Journal of Operational Research, Elsevier, vol. 264(2), pages 440-452.
    8. Yang, Bill Huajian, 2019. "Monotonic Estimation for the Survival Probability over a Risk-Rated Portfolio by Discrete-Time Hazard Rate Models," MPRA Paper 93398, University Library of Munich, Germany.

    More about this item

    Keywords

    classification tree; decision tree; monotone; monotonicity constraint; ordinal data;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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