Optimization of β-decision rules relative to number of misclassifications

Handle URI:
http://hdl.handle.net/10754/564505
Title:
Optimization of β-decision rules relative to number of misclassifications
Authors:
Zielosko, Beata
Abstract:
In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Springer Science + Business Media
Journal:
Computational Collective Intelligence. Technologies and Applications
Conference/Event name:
4th International Conference on Computational Collective Intelligence, ICCCI 2012
Issue Date:
2012
DOI:
10.1007/978-3-642-34707-8_35
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783642347061
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZielosko, Beataen
dc.date.accessioned2015-08-04T07:02:42Zen
dc.date.available2015-08-04T07:02:42Zen
dc.date.issued2012en
dc.identifier.isbn9783642347061en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-34707-8_35en
dc.identifier.urihttp://hdl.handle.net/10754/564505en
dc.description.abstractIn the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectapproximate decision rulesen
dc.subjectdynamic programmingen
dc.subjectnumber of misclassificationsen
dc.titleOptimization of β-decision rules relative to number of misclassificationsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalComputational Collective Intelligence. Technologies and Applicationsen
dc.conference.date28 November 2012 through 30 November 2012en
dc.conference.name4th International Conference on Computational Collective Intelligence, ICCCI 2012en
dc.conference.locationHo Chi Minh Cityen
dc.contributor.institutionInstitute of Computer Science, University of Silesia, 39, Bȩdzińska St., 41-200 Sosnowiec, Polanden
kaust.authorZielosko, Beataen
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