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dc.contributor.authorChikalov, Igor
dc.date.accessioned2015-08-03T09:02:04Z
dc.date.available2015-08-03T09:02:04Z
dc.date.issued2011
dc.identifier.isbn9783642226601
dc.identifier.issn18684394
dc.identifier.doi10.1007/978-3-642-22661-8_4
dc.identifier.urihttp://hdl.handle.net/10754/561677
dc.description.abstractThe study of algorithms for decision tree construction was initiated in 1960s. The first algorithms are based on the separation heuristic [13, 31] that at each step tries dividing the set of objects as evenly as possible. Later Garey and Graham [28] showed that such algorithm may construct decision trees whose average depth is arbitrarily far from the minimum. Hyafil and Rivest in [35] proved NP-hardness of DT problem that is constructing a tree with the minimum average depth for a diagnostic problem over 2-valued information system and uniform probability distribution. Cox et al. in [22] showed that for a two-class problem over information system, even finding the root node attribute for an optimal tree is an NP-hard problem. © Springer-Verlag Berlin Heidelberg 2011.
dc.publisherSpringer Nature
dc.titleAlgorithms for Decision Tree Construction
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalAverage Time Complexity of Decision Trees
kaust.personChikalov, Igor


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