Classifiers based on optimal decision rules

Handle URI:
http://hdl.handle.net/10754/563101
Title:
Classifiers based on optimal decision rules
Authors:
Amin, Talha ( 0000-0003-3035-8612 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Zielosko, Beata
Abstract:
Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Publisher:
IOS Press
Journal:
Fundamenta Informaticae
Issue Date:
25-Nov-2013
DOI:
10.3233/FI-2013-901
Type:
Article
ISSN:
01692968
Sponsors:
This research was supported by King Abdullah University of Science and Technology in the frameworks of joint project with Nizhni Novgorod State University "Novel Algorithms in Machine Learning and Computer Vision, and Their High Performance Implementations", Russian Federal Program "Research and Development in Prioritized Directions of Scientific-Technological Complex of Russia in 2007-2013". The authors wish to express their gratitude to anonymous reviewers for useful comments.
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAmin, Talhaen
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.contributor.authorZielosko, Beataen
dc.date.accessioned2015-08-03T11:35:47Zen
dc.date.available2015-08-03T11:35:47Zen
dc.date.issued2013-11-25en
dc.identifier.issn01692968en
dc.identifier.doi10.3233/FI-2013-901en
dc.identifier.urihttp://hdl.handle.net/10754/563101en
dc.description.abstractBased on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).en
dc.description.sponsorshipThis research was supported by King Abdullah University of Science and Technology in the frameworks of joint project with Nizhni Novgorod State University "Novel Algorithms in Machine Learning and Computer Vision, and Their High Performance Implementations", Russian Federal Program "Research and Development in Prioritized Directions of Scientific-Technological Complex of Russia in 2007-2013". The authors wish to express their gratitude to anonymous reviewers for useful comments.en
dc.publisherIOS Pressen
dc.subjectclassifiersen
dc.subjectdecision rulesen
dc.subjectDynamic programmingen
dc.titleClassifiers based on optimal decision rulesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Groupen
dc.identifier.journalFundamenta Informaticaeen
dc.contributor.institutionInstitute of Computer Science, University of Silesia, 39, Bȩdzińska St., Sosnowiec 41-200, Polanden
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
kaust.authorZielosko, Beataen
kaust.authorAmin, Talhaen
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