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dc.contributor.authorBouckaert, Remco R.en_US
dc.date.accessioned2008-03-19T04:58:16Z
dc.date.available2007-05-09en_US
dc.date.available2008-03-19T04:58:16Z
dc.date.issued2004-09-01en_US
dc.identifier.citationBouckaert, R. R., (2004). Bayesian network classifiers in Weka. (Working paper series. University of Waikato, Department of Computer Science. No. 14/2004). Hamilton, New Zealand: University of Waikato.en_US
dc.identifier.urihttps://hdl.handle.net/10289/85
dc.description.abstractVarious Bayesian network classifier learning algorithms are implemented in Weka [10].This note provides some user documentation and implementation details. Summary of main capabilities: _Structure learning of Bayesian networks using various hill climbing (K2, B, etc) and general purpose (simulated annealing, tabu search) algorithms. _Local score metrics implemented; Bayes, BDe, MDL, entropy, AIC. _Global score metrics implemented; leave one out cv, k-fold cv and cumulative cv. _Conditional independence based causal recovery algorithm available. _Parameter estimation using direct estimates and Bayesian model averaging. _GUI for easy inspection of Bayesian networks. _Part of Weka allowing systematic experiments to compare Bayes net performance with general purpose classi_ers like C4.5, nearest neighbor, support vector, etc. _Source code available under GPL allows for integration in other systems and makes it easy to extend.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherDepartment of Computer Scienceen_NZ
dc.titleBayesian network classifiers in Wekaen_US
dc.typeWorking Paperen_US
pubs.elements-id52837
pubs.place-of-publicationWaikato University, Hamiltonen_NZ


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