Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis

Handle URI:
http://hdl.handle.net/10754/625914
Title:
Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis
Authors:
Chikalov, Igor; Hussain, Shahid ( 0000-0002-1698-2809 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
This paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Chikalov I, Hussain S, Moshkov M (2017) Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.10.021.
Publisher:
Elsevier BV
Journal:
European Journal of Operational Research
Issue Date:
19-Oct-2017
DOI:
10.1016/j.ejor.2017.10.021
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/S0377221717309347
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorChikalov, Igoren
dc.contributor.authorHussain, Shahiden
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2017-10-22T11:48:13Z-
dc.date.available2017-10-22T11:48:13Z-
dc.date.issued2017-10-19en
dc.identifier.citationChikalov I, Hussain S, Moshkov M (2017) Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.10.021.en
dc.identifier.issn0377-2217en
dc.identifier.doi10.1016/j.ejor.2017.10.021en
dc.identifier.urihttp://hdl.handle.net/10754/625914-
dc.description.abstractThis paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms.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/S0377221717309347en
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, 18 October 2017. DOI: 10.1016/j.ejor.2017.10.021. © 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.subjectbi criteria optimizationen
dc.subjectDynamic programmingen
dc.subjectDecision treesen
dc.subjectPareto optimal pointsen
dc.subjectHeuristicsen
dc.titleBi-Criteria Optimization of Decision Trees with Applications to Data Analysisen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalEuropean Journal of Operational Researchen
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Science and Engineering, Habib University, Karachi, Pakistanen
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
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