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    Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis

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    Type
    Article
    Authors
    Chikalov, Igor
    Hussain, Shahid cc
    Moshkov, Mikhail cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Office of the VP
    Date
    2017-10-19
    Online Publication Date
    2017-10-19
    Print Publication Date
    2018-04
    Permanent link to this record
    http://hdl.handle.net/10754/625914
    
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    Abstract
    This paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms.
    Citation
    Chikalov I, Hussain S, Moshkov M (2017) Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.10.021.
    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.2017.10.021
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0377221717309347
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ejor.2017.10.021
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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