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dc.contributor.authorAlkhalid, Abdulaziz
dc.contributor.authorChikalov, Igor
dc.contributor.authorMoshkov, Mikhail
dc.date.accessioned2015-08-04T06:20:34Z
dc.date.available2015-08-04T06:20:34Z
dc.date.issued2010
dc.identifier.isbn3642135285; 9783642135286
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-642-13529-3_47
dc.identifier.urihttp://hdl.handle.net/10754/564246
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.
dc.publisherSpringer Nature
dc.subjectAlgorithm complexity
dc.subjectDecision tree
dc.subjectDynamic programming
dc.titleOn algorithm for building of optimal α-decision trees
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
dc.identifier.journalRough Sets and Current Trends in Computing
dc.conference.date28 June 2010 through 30 June 2010
dc.conference.name7th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2010
dc.conference.locationWarsaw
kaust.personChikalov, Igor
kaust.personMoshkov, Mikhail
kaust.personAlkhalid, Abdulaziz


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