Extensions of dynamic programming as a new tool for decision tree optimization

Handle URI:
http://hdl.handle.net/10754/562512
Title:
Extensions of dynamic programming as a new tool for decision tree optimization
Authors:
Alkhalid, Abdulaziz; Chikalov, Igor; Hussain, Shahid ( 0000-0002-1698-2809 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
The chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study possibilities of sequential optimization of α-decision trees relative to different cost functions such as depth, average depth, and number of nodes. For decision trees, we analyze relationships between depth and number of misclassifications. We also discuss results of computer experiments with some datasets from UCI ML Repository. ©Springer-Verlag Berlin Heidelberg 2013.
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; Computer Science Program
Publisher:
Springer Science + Business Media
Journal:
Smart Innovation, Systems and Technologies
Issue Date:
2013
DOI:
10.1007/978-3-642-28699-5_2
Type:
Article
ISSN:
21903018
ISBN:
9783642286988
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer 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.authorHussain, Shahiden
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-03T10:40:51Zen
dc.date.available2015-08-03T10:40:51Zen
dc.date.issued2013en
dc.identifier.isbn9783642286988en
dc.identifier.issn21903018en
dc.identifier.doi10.1007/978-3-642-28699-5_2en
dc.identifier.urihttp://hdl.handle.net/10754/562512en
dc.description.abstractThe chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study possibilities of sequential optimization of α-decision trees relative to different cost functions such as depth, average depth, and number of nodes. For decision trees, we analyze relationships between depth and number of misclassifications. We also discuss results of computer experiments with some datasets from UCI ML Repository. ©Springer-Verlag Berlin Heidelberg 2013.en
dc.publisherSpringer Science + Business Mediaen
dc.titleExtensions of dynamic programming as a new tool for decision tree 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.contributor.departmentComputer Science Programen
dc.identifier.journalSmart Innovation, Systems and Technologiesen
kaust.authorChikalov, Igoren
kaust.authorHussain, Shahiden
kaust.authorMoshkov, Mikhailen
kaust.authorAlkhalid, Abdulazizen
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