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dc.contributor.authorAlkhalid, Abdulaziz
dc.contributor.authorChikalov, Igor
dc.contributor.authorMoshkov, Mikhail
dc.date.accessioned2015-08-04T06:23:52Z
dc.date.available2015-08-04T06:23:52Z
dc.date.issued2011
dc.identifier.isbn9783642244247
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-642-24425-4_25
dc.identifier.urihttp://hdl.handle.net/10754/564329
dc.description.abstractA comparison among different heuristics that are used by greedy algorithms which constructs approximate decision trees (α-decision trees) is presented. The comparison is conducted using decision tables based on 24 data sets from UCI Machine Learning Repository [2]. Complexity of decision trees is estimated relative to several cost functions: depth, average depth, number of nodes, number of nonterminal nodes, and number of terminal nodes. Costs of trees built by greedy algorithms are compared with minimum costs calculated by an algorithm based on dynamic programming. The results of experiments assign to each cost function a set of potentially good heuristics that minimize it. © 2011 Springer-Verlag.
dc.publisherSpringer Science + Business Media
dc.subjectDecision tree
dc.subjectdynamic programming
dc.subjectgreedy algorithm
dc.titleComparison of greedy algorithms for α-decision tree construction
dc.typeConference Paper
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.identifier.journalRough Sets and Knowledge Technology
dc.conference.date9 October 2011 through 12 October 2011
dc.conference.name6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011
dc.conference.locationBanff, AB
kaust.personChikalov, Igor
kaust.personMoshkov, Mikhail
kaust.personAlkhalid, Abdulaziz


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