Optimization of decision rules based on dynamic programming approach

Handle URI:
http://hdl.handle.net/10754/563341
Title:
Optimization of decision rules based on dynamic programming approach
Authors:
Zielosko, Beata; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Amin, Talha ( 0000-0003-3035-8612 )
Abstract:
This chapter is devoted to the study of an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure that is the difference between number of rows in a given decision table and the number of rows labeled with the most common decision for this table divided by the number of rows in the decision table. We fix a threshold γ, such that 0 ≤ γ < 1, and study so-called γ-decision rules (approximate decision rules) that localize rows in subtables which uncertainty is at most γ. Presented algorithm constructs a directed acyclic graph Δ γ T which nodes are subtables of the decision table T given by pairs "attribute = value". The algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The chapter contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2014 Springer International Publishing Switzerland.
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
Journal:
Studies in Computational Intelligence
Issue Date:
14-Jan-2014
DOI:
10.1007/978-3-319-01866-9-12
Type:
Article
ISSN:
1860949X
ISBN:
9783319018652
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.authorZielosko, Beataen
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.contributor.authorAmin, Talhaen
dc.date.accessioned2015-08-03T11:46:11Zen
dc.date.available2015-08-03T11:46:11Zen
dc.date.issued2014-01-14en
dc.identifier.isbn9783319018652en
dc.identifier.issn1860949Xen
dc.identifier.doi10.1007/978-3-319-01866-9-12en
dc.identifier.urihttp://hdl.handle.net/10754/563341en
dc.description.abstractThis chapter is devoted to the study of an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure that is the difference between number of rows in a given decision table and the number of rows labeled with the most common decision for this table divided by the number of rows in the decision table. We fix a threshold γ, such that 0 ≤ γ < 1, and study so-called γ-decision rules (approximate decision rules) that localize rows in subtables which uncertainty is at most γ. Presented algorithm constructs a directed acyclic graph Δ γ T which nodes are subtables of the decision table T given by pairs "attribute = value". The algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The chapter contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2014 Springer International Publishing Switzerland.en
dc.publisherSpringer Scienceen
dc.titleOptimization of decision rules based on dynamic programming approachen
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.journalStudies in Computational Intelligenceen
dc.contributor.institutionInstitute of Computer Science, University of Silesia, 39, Bȩdzińska St, 41-200 Sosnowiec, Polanden
kaust.authorZielosko, Beataen
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
kaust.authorAmin, Talhaen
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