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Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

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  • Azad, Mohammad
  • Moshkov, Mikhail

Abstract

We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.

Suggested Citation

  • Azad, Mohammad & Moshkov, Mikhail, 2017. "Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions," European Journal of Operational Research, Elsevier, vol. 263(3), pages 910-921.
    • Handle: RePEc:eee:ejores:v:263:y:2017:i:3:p:910-921
    DOI: 10.1016/j.ejor.2017.06.026
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    5. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
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    2. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.

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