Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions
dc.contributor.author | Azad, Mohammad | |
dc.contributor.author | Moshkov, Mikhail | |
dc.date.accessioned | 2017-06-19T09:21:45Z | |
dc.date.available | 2017-06-19T09:21:45Z | |
dc.date.issued | 2017-06-16 | |
dc.identifier.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. | |
dc.identifier.issn | 0377-2217 | |
dc.identifier.doi | 10.1016/j.ejor.2017.06.026 | |
dc.identifier.uri | http://hdl.handle.net/10754/625062 | |
dc.description.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. | |
dc.description.sponsorship | 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. | |
dc.publisher | Elsevier BV | |
dc.relation.url | http://www.sciencedirect.com/science/article/pii/S0377221717305659 | |
dc.rights | NOTICE: 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/ | |
dc.subject | Multiple criteria analysis | |
dc.subject | Dynamic programming | |
dc.subject | Decision trees | |
dc.subject | Inhibitory trees | |
dc.subject | Totally optimal trees | |
dc.title | Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions | |
dc.type | Article | |
dc.contributor.department | Applied Mathematics and Computational Science Program | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.identifier.journal | European Journal of Operational Research | |
dc.eprint.version | Post-print | |
kaust.person | Azad, Mohammad | |
kaust.person | Moshkov, Mikhail | |
dc.date.published-online | 2017-06-16 | |
dc.date.published-print | 2017-12 |
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Applied Mathematics and Computational Science Program
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Computer Science Program
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Computer Science Program
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Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
For more information visit: https://cemse.kaust.edu.sa/