On algorithm for building of optimal α-decision trees

Handle URI:
http://hdl.handle.net/10754/564246
Title:
On algorithm for building of optimal α-decision trees
Authors:
Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.
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 Current Trends in Computing
Conference/Event name:
7th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2010
Issue Date:
2010
DOI:
10.1007/978-3-642-13529-3_47
Type:
Conference Paper
ISSN:
03029743
ISBN:
3642135285; 9783642135286
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.authorAlkhalid, Abdulazizen
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-04T06:20:34Zen
dc.date.available2015-08-04T06:20:34Zen
dc.date.issued2010en
dc.identifier.isbn3642135285; 9783642135286en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-13529-3_47en
dc.identifier.urihttp://hdl.handle.net/10754/564246en
dc.description.abstractThe paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectAlgorithm complexityen
dc.subjectDecision treeen
dc.subjectDynamic programmingen
dc.titleOn algorithm for building of optimal α-decision treesen
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 Current Trends in Computingen
dc.conference.date28 June 2010 through 30 June 2010en
dc.conference.name7th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2010en
dc.conference.locationWarsawen
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
kaust.authorAlkhalid, Abdulazizen
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