Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

Handle URI:
http://hdl.handle.net/10754/625062
Title:
Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Azad M, Moshkov M (2017) Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.06.026.
Publisher:
Elsevier BV
Journal:
European Journal of Operational Research
Issue Date:
16-Jun-2017
DOI:
10.1016/j.ejor.2017.06.026
Type:
Article
ISSN:
0377-2217
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.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0377221717305659
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAzad, Mohammaden
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2017-06-19T09:21:45Z-
dc.date.available2017-06-19T09:21:45Z-
dc.date.issued2017-06-16en
dc.identifier.citationAzad M, Moshkov M (2017) Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.06.026.en
dc.identifier.issn0377-2217en
dc.identifier.doi10.1016/j.ejor.2017.06.026en
dc.identifier.urihttp://hdl.handle.net/10754/625062-
dc.description.abstractWe study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.en
dc.description.sponsorshipResearch 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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0377221717305659en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, [, , (2017-06-16)] DOI: 10.1016/j.ejor.2017.06.026 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectMultiple criteria analysisen
dc.subjectDynamic programmingen
dc.subjectDecision treesen
dc.subjectInhibitory treesen
dc.subjectTotally optimal treesen
dc.titleMulti-stage optimization of decision and inhibitory trees for decision tables with many-valued decisionsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalEuropean Journal of Operational Researchen
dc.eprint.versionPost-printen
kaust.authorAzad, Mohammaden
kaust.authorMoshkov, Mikhailen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.