Optimization of Approximate Inhibitory Rules Relative to Number of Misclassifications

Handle URI:
http://hdl.handle.net/10754/552476
Title:
Optimization of Approximate Inhibitory Rules Relative to Number of Misclassifications
Authors:
Alsolami, Fawaz ( 0000-0001-5858-4908 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Zielosko, Beata
Abstract:
In this work, we consider so-called nonredundant inhibitory rules, containing an expression “attribute:F value” on the right- hand side, for which the number of misclassifications is at most a threshold γ. We study a dynamic programming approach for description of the considered set of rules. This approach allows also the optimization of nonredundant inhibitory rules relative to the length and coverage. The aim of this paper is to investigate an additional possibility of optimization relative to the number of misclassifications. The results of experiments with decision tables from the UCI Machine Learning Repository show this additional optimization achieves a fewer misclassifications. Thus, the proposed optimization procedure is promising.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Optimization of Approximate Inhibitory Rules Relative to Number of Misclassifications 2013, 22:295 Procedia Computer Science
Journal:
Procedia Computer Science
Issue Date:
4-Oct-2013
DOI:
10.1016/j.procs.2013.09.106
Type:
Article
ISSN:
18770509
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1877050913008995
Appears in Collections:
Articles; 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.contributor.authorZielosko, Beataen
dc.date.accessioned2015-05-07T14:10:34Zen
dc.date.available2015-05-07T14:10:34Zen
dc.date.issued2013-10-04en
dc.identifier.citationOptimization of Approximate Inhibitory Rules Relative to Number of Misclassifications 2013, 22:295 Procedia Computer Scienceen
dc.identifier.issn18770509en
dc.identifier.doi10.1016/j.procs.2013.09.106en
dc.identifier.urihttp://hdl.handle.net/10754/552476en
dc.description.abstractIn this work, we consider so-called nonredundant inhibitory rules, containing an expression “attribute:F value” on the right- hand side, for which the number of misclassifications is at most a threshold γ. We study a dynamic programming approach for description of the considered set of rules. This approach allows also the optimization of nonredundant inhibitory rules relative to the length and coverage. The aim of this paper is to investigate an additional possibility of optimization relative to the number of misclassifications. The results of experiments with decision tables from the UCI Machine Learning Repository show this additional optimization achieves a fewer misclassifications. Thus, the proposed optimization procedure is promising.en
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1877050913008995en
dc.rightsArchived with thanks to Procedia Computer Science. http://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectinhibitory rulesen
dc.subjectnumber of misclassificationsen
dc.subjectdynamic programmingen
dc.titleOptimization of Approximate Inhibitory Rules Relative to Number of Misclassificationsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProcedia Computer Scienceen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionComputer Science Department, King Abdulaziz University, Saudi Arabiaen
dc.contributor.institutionInstitute of Computer Science, University of Silesia, 39, Be¸dzińska St., Sosnowiec 41-200, Polanden
kaust.authorAlsolami, Fawazen
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
kaust.authorZielosko, Beataen
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