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    Experimental Study of Totally Optimal Decision Trees

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    tot_opt_trees_FI.pdf
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    Type
    Article
    Authors
    Aldilaijan, Abdulla
    Azad, Mohammad cc
    Moshkov, Mikhail cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Applied Mathematics and Computational Science Program
    Date
    2019-03-22
    Online Publication Date
    2019-03-22
    Print Publication Date
    2019-03-14
    Permanent link to this record
    http://hdl.handle.net/10754/631801
    
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    Abstract
    In this paper, we present results of experimental studies related to the existence of totally optimal decision trees (which are optimal relative to two or more cost functions simultaneously) for nine decision tables from the UCI Machine Learning Repository. Such trees can be useful when we consider decision trees as algorithms for problem solving or as a way for knowledge representation. For cost functions, we use depth, average depth, and number of nodes. We study not only exact but also approximate decision trees based on five uncertainty measures: entropy, Gini index, misclassification error, relative misclassification error, and number of unordered pairs of rows with different decisions. To investigate the existence of totally optimal trees, we use an extension of dynamic programming that allows us to make multi-stage optimization of decision trees relative to a sequence of cost functions. Experimental results show that totally optimal decision trees exist in many cases. The behavior of graphs that describe how the number of decision tables with totally optimal decision trees depends on their accuracy is mainly irregular. However, one can observe some trends, in particular, an upward trend when accuracy is decreasing.
    Citation
    Abdulla Aldilaijan, Mohammad Azad, Mikhail Moshkov. Experimental Study of Totally Optimal Decision Trees. FI. IOS Press; 2019;165: 245–261. doi:10.3233/FI-2019-1784
    Sponsors
    Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). We are greatly indebted to the anonymous reviewers for useful comments and suggestions.
    Publisher
    IOS Press
    Journal
    Fundamenta Informaticae
    DOI
    10.3233/FI-2019-1784
    Additional Links
    https://content.iospress.com/articles/fundamenta-informaticae/fi1784
    ae974a485f413a2113503eed53cd6c53
    10.3233/FI-2019-1784
    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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