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

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    Type
    Article
    Authors
    Azad, Mohammad cc
    Moshkov, Mikhail cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-06-16
    Online Publication Date
    2017-06-16
    Print Publication Date
    2017-12
    Permanent link to this record
    http://hdl.handle.net/10754/625062
    
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    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.
    Citation
    Azad M, Moshkov M (2017) Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.06.026.
    Sponsors
    Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We are greatly indebted to the anonymous reviewers for useful comments and suggestions.
    Publisher
    Elsevier BV
    Journal
    European Journal of Operational Research
    DOI
    10.1016/j.ejor.2017.06.026
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0377221717305659
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ejor.2017.06.026
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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