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dc.contributor.authorJunque de Fortuny, Enric-
dc.contributor.authorMartens, David-
dc.contributor.authorProvost, Foster-
dc.date.accessioned2013-12-20T21:21:39Z-
dc.date.available2013-12-20T21:21:39Z-
dc.date.issued2013-12-20-
dc.identifier.urihttp://hdl.handle.net/2451/33545-
dc.description.abstractTraditional event models underlying naive Bayes classifiers assume probability distributions that are not appropriate for binary data generated by human behaviour. In this work, we develop a new event model, based on a somewhat forgotten distribution created by Kenneth Ted Wallenius in 1963. We show that it achieves superior performance using less data on a collection of Facebook datasets, where the task is to predict personality traits, based on likes.en_US
dc.description.sponsorshipFaculty of Applied Economics, University of Antwerp, Belgium; Department of Information, Operations & Management Sciences, NYU Stern School of Businessen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesCBA-13-05-
dc.titleWallenius Naive Bayesen_US
dc.typeWorking Paperen_US
Appears in Collections:Center for Business Analytics Working Papers

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