Decision rule classifiers for multi-label decision tables

Handle URI:
http://hdl.handle.net/10754/564858
Title:
Decision rule classifiers for multi-label decision tables
Authors:
Alsolami, Fawaz ( 0000-0001-5858-4908 ) ; Azad, Mohammad ( 0000-0001-9851-1420 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.
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 Intelligent Systems Paradigms
Conference/Event name:
2nd International Conference on Rough Sets and Emerging Intelligent Systems Paradigms, RSEISP 2014 - Held as Part of 2014 Joint Rough Set Symposium, JRS 2014
Issue Date:
2014
DOI:
10.1007/978-3-319-08729-0_18
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783319087283
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.authorAzad, Mohammaden
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-04T07:23:18Zen
dc.date.available2015-08-04T07:23:18Zen
dc.date.issued2014en
dc.identifier.isbn9783319087283en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-319-08729-0_18en
dc.identifier.urihttp://hdl.handle.net/10754/564858en
dc.description.abstractRecently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectclassificationen
dc.subjectdecision rulesen
dc.subjectrule heuristicsen
dc.titleDecision rule classifiers for multi-label decision tablesen
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 Intelligent Systems Paradigmsen
dc.conference.date9 July 2014 through 13 July 2014en
dc.conference.name2nd International Conference on Rough Sets and Emerging Intelligent Systems Paradigms, RSEISP 2014 - Held as Part of 2014 Joint Rough Set Symposium, JRS 2014en
dc.conference.locationGranada and Madriden
dc.contributor.institutionComputer Science Department, King Abdulaziz University, Saudi Arabiaen
kaust.authorAlsolami, Fawazen
kaust.authorAzad, Mohammaden
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
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