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dc.contributor.authorHilbert, Andreasen
dc.date.accessioned2009-01-29T15:09:27Z-
dc.date.available2009-01-29T15:09:27Z-
dc.date.issued2002-
dc.identifier.urihttp://hdl.handle.net/10419/22816-
dc.description.abstractDecision trees are used very successfully for the identification resp. classification task of objects in many domains like marketing (e.g. Decker, Temme (2001)) or medicine. Other procedures to classify objects are for instance the logistic regression, the logit- or probit analysis, the linear or squared discriminant analysis, the nearest neighbour procedure or some kernel density estimators. The common aim of all these classification procedures is to generate classification rules which describe the correlation between some independent exogenous variables resp. attributes and at least one endogenous variable, the so called class membership variable.en
dc.language.isoengen
dc.publisher|aUniversität Augsburg, Institut für Statistik und Mathematische Wirtschaftstheorie |cAugsburgen
dc.relation.ispartofseries|aArbeitspapiere zur mathematischen Wirtschaftsforschung |x180en
dc.subject.ddc330en
dc.subject.stwEntscheidungsbaumen
dc.subject.stwKorrelationen
dc.subject.stwTheorieen
dc.titleSome Remarks about the Usage of Asymmetric Correlation Measurements for the Induction of Decision Trees-
dc.typeWorking Paperen
dc.identifier.ppn379948885en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:augamw:180en

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