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dc.contributor.authorAzad, Mohammad
dc.contributor.authorChikalov, Igor
dc.contributor.authorMoshkov, Mikhail
dc.date.accessioned2020-11-04T12:40:19Z
dc.date.available2020-11-04T12:40:19Z
dc.date.issued2020-10-02
dc.identifier.citationAzad, M., Chikalov, I., & Moshkov, M. (2020). Representation of Knowledge by Decision Trees for Decision Tables with Multiple Decisions. Procedia Computer Science, 176, 653–659. doi:10.1016/j.procs.2020.09.037
dc.identifier.issn1877-0509
dc.identifier.doi10.1016/j.procs.2020.09.037
dc.identifier.urihttp://hdl.handle.net/10754/665815
dc.description.abstractIn this paper, we study decisions trees for decision tables with multiple decisions as a means for knowledge representation. To this end, we consider three methods to design decision trees and evaluate the number of nodes, and local and global misclassification rates of constructed trees. The considered methods are based on a dynamic programming algorithm for bi-objective optimization of decision trees. The goal of this study is to construct trees with reasonable number of nodes and at the same time reasonable accuracy. Previously, it was mentioned that the consideration of only the global misclassification rate of the decision tree is not enough and it is necessary to study also the local misclassification rate. The reason is that even if the global misclassification rate related to the whole tree is enough small, the local misclassification rate related to the terminal nodes of the tree can be too big. One of the considered methods allows us to construct the decision trees with moderate number of nodes as well as moderate global and local misclassification rates. These decision trees can be used for the knowledge representation.
dc.description.sponsorshipResearch reported in this publication was supported by Jouf University and by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to the anonymous reviewers for useful comments.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S1877050920319323
dc.rightsThis is an open access article.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleRepresentation of knowledge by decision trees for decision tables with multiple decisions
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
dc.conference.date2020-09-16 to 2020-09-18
dc.conference.name24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020
dc.conference.locationVirtual Online
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionJouf University, College of Computer and Information Sciences, Sakaka 72441, Saudi Arabia
dc.contributor.institutionIntel Corporation, 5000 W Chandler Blvd, Chandler, AZ 85226, USA
dc.identifier.volume176
dc.identifier.pages653-659
kaust.personMoshkov, Mikhail
dc.identifier.eid2-s2.0-85093356758
refterms.dateFOA2020-11-04T12:43:34Z
dc.date.published-online2020-10-02
dc.date.published-print2020


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