Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications

Handle URI:
http://hdl.handle.net/10754/564663
Title:
Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications
Authors:
Alsolami, Fawaz ( 0000-0001-5858-4908 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. © 2013 Springer-Verlag.
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:
Springer Science + Business Media
Journal:
Rough Sets and Knowledge Technology
Conference/Event name:
8th International Conference on Rough Sets and Knowledge Technology, RSKT 2013
Issue Date:
2013
DOI:
10.1007/978-3-642-41299-8_15
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783642412981
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlsolami, Fawazen
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-04T07:11:25Zen
dc.date.available2015-08-04T07:11:25Zen
dc.date.issued2013en
dc.identifier.isbn9783642412981en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-41299-8_15en
dc.identifier.urihttp://hdl.handle.net/10754/564663en
dc.description.abstractThis paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed. © 2013 Springer-Verlag.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectcoverageen
dc.subjectdynamic programmingen
dc.subjectinhibitory rulesen
dc.subjectlengthen
dc.subjectnumber of misclassificationsen
dc.titleSequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassificationsen
dc.typeConference Paperen
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.journalRough Sets and Knowledge Technologyen
dc.conference.date11 October 2013 through 14 October 2013en
dc.conference.name8th International Conference on Rough Sets and Knowledge Technology, RSKT 2013en
dc.conference.locationHalifax, NSen
dc.contributor.institutionComputer Science Department, King Abdulaziz University, Saudi Arabiaen
kaust.authorAlsolami, Fawazen
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
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