Dynamic programming approach for partial decision rule optimization

Handle URI:
http://hdl.handle.net/10754/564619
Title:
Dynamic programming approach for partial decision rule optimization
Authors:
Amin, Talha ( 0000-0003-3035-8612 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 ) ; Zielosko, Beata
Abstract:
This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
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:
IOS Press
Journal:
Fundamenta Informaticae
Issue Date:
4-Oct-2012
DOI:
10.3233/FI-2012-735
Type:
Article
ISSN:
01692968
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.authorAmin, Talhaen
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.contributor.authorZielosko, Beataen
dc.date.accessioned2015-08-04T07:05:17Zen
dc.date.available2015-08-04T07:05:17Zen
dc.date.issued2012-10-04en
dc.identifier.issn01692968en
dc.identifier.doi10.3233/FI-2012-735en
dc.identifier.urihttp://hdl.handle.net/10754/564619en
dc.description.abstractThis paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.en
dc.publisherIOS Pressen
dc.subjectcoverageen
dc.subjectdynamic programmingen
dc.subjectlengthen
dc.subjectPartial decision rulesen
dc.titleDynamic programming approach for partial decision rule optimizationen
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.journalFundamenta Informaticaeen
dc.contributor.institutionInstitute of Computer Science, University of Silesia, 39, Bdzińska St., Sosnowiec 41-200, Polanden
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
kaust.authorZielosko, Beataen
kaust.authorAmin, Talhaen
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