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    Decision Rules Derived from Optimal Decision Trees with Hypotheses

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    entropy-23-01641.pdf
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    Type
    Article
    Authors
    Azad, Mohammad cc
    Chikalov, Igor cc
    Hussain, Shahid cc
    Moshkov, Mikhail cc
    Zielosko, Beata cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
    Date
    2021-12-07
    Permanent link to this record
    http://hdl.handle.net/10754/673940
    
    Metadata
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    Abstract
    Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses. This kind of query is similar to the equivalence queries considered in exact learning. Earlier, we designed dynamic programming algorithms for the computation of the minimum depth and the minimum number of internal nodes in decision trees that have hypotheses. Modification of these algorithms considered in the present paper permits us to build decision trees with hypotheses that are optimal relative to the depth or relative to the number of the internal nodes. We compare the length and coverage of decision rules extracted from optimal decision trees with hypotheses and decision rules extracted from optimal conventional decision trees to choose the ones that are preferable as a tool for the representation of information. To this end, we conduct computer experiments on various decision tables from the UCI Machine Learning Repository. In addition, we also consider decision tables for randomly generated Boolean functions. The collected results show that the decision rules derived from decision trees with hypotheses in many cases are better than the rules extracted from conventional decision trees.
    Citation
    Azad, M., Chikalov, I., Hussain, S., Moshkov, M., & Zielosko, B. (2021). Decision Rules Derived from Optimal Decision Trees with Hypotheses. Entropy, 23(12), 1641. doi:10.3390/e23121641
    Sponsors
    Research funded by King Abdullah University of Science and Technology.
    Publisher
    MDPI AG
    Journal
    Entropy
    DOI
    10.3390/e23121641
    Additional Links
    https://www.mdpi.com/1099-4300/23/12/1641
    ae974a485f413a2113503eed53cd6c53
    10.3390/e23121641
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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