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Monotone Decision Trees and Noisy Data

Author

Listed:
  • Bioch, J.C.
  • Popova, V.

Abstract

The decision tree algorithm for monotone classification presented in [4, 10] requires strictly monotone data sets. This paper addresses the problem of noise due to violation of the monotonicity constraints and proposes a modification of the algorithm to handle noisy data. It also presents methods for controlling the size of the resulting trees while keeping the monotonicity property whether the data set is monotone or not.

Suggested Citation

  • 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.
  • Handle: RePEc:ems:eureri:207
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    File URL: https://repub.eur.nl/pub/207/erimrs20020617115748.pdf
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    Citations

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    Cited by:

    1. 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.

    More about this item

    Keywords

    monotone decision trees; noise; ordinal classification; pruning;
    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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