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dc.contributor.authorMoshkov, Mikhail
dc.date.accessioned2021-11-04T10:36:31Z
dc.date.available2021-04-04T07:58:43Z
dc.date.available2021-11-04T10:36:31Z
dc.date.issued2021-07
dc.date.submitted2020-07-09
dc.identifier.citationMoshkov, M. (2021). On the depth of decision trees over infinite 1-homogeneous binary information systems. Array, 100060. doi:10.1016/j.array.2021.100060
dc.identifier.issn2590-0056
dc.identifier.doi10.1016/j.array.2021.100060
dc.identifier.urihttp://hdl.handle.net/10754/668498
dc.description.abstractIn this paper, we study decision trees, which solve problems defined over a specific subclass of infinite information systems, namely: 1-homogeneous binary information systems. It is proved that the minimum depth of a decision tree (defined as a function on the number of attributes in a problem’s description) grows – in the worst case – logarithmically or linearly for each information system in this class. We consider a number of examples of infinite 1-homogeneous binary information systems, including one closely related to the decision trees constructed by the CART algorithm.
dc.description.sponsorshipResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The author is thankful to Dr. Michal Mankowski for the helpful comments. The author gratefully acknowledges the useful suggestions of the anonymous reviewers.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S2590005621000084
dc.rights© 2021 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleOn the depth of decision trees over infinite 1-homogeneous binary information systems
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
dc.identifier.journalArray
dc.eprint.versionPublisher's Version/PDF
dc.identifier.pages100060
pubs.publication-statusPublished
kaust.personMoshkov, Mikhail
dc.date.accepted2021-03-22
refterms.dateFOA2021-04-04T08:04:37Z


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© 2021 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2021 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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