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dc.contributor.authorAlkhalid, Abdulaziz
dc.contributor.authorChikalov, Igor
dc.contributor.authorHussain, Shahid
dc.contributor.authorMoshkov, Mikhail
dc.date.accessioned2015-08-03T10:40:51Z
dc.date.available2015-08-03T10:40:51Z
dc.date.issued2013
dc.identifier.isbn9783642286988
dc.identifier.issn21903018
dc.identifier.doi10.1007/978-3-642-28699-5_2
dc.identifier.urihttp://hdl.handle.net/10754/562512
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.
dc.publisherSpringer Nature
dc.titleExtensions of dynamic programming as a new tool for decision tree optimization
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
dc.contributor.departmentComputer Science Program
dc.identifier.journalSmart Innovation, Systems and Technologies
kaust.personChikalov, Igor
kaust.personHussain, Shahid
kaust.personMoshkov, Mikhail
kaust.personAlkhalid, Abdulaziz


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