| dc.contributor.author | Bouckaert, Remco R. | en_US |
| dc.date.accessioned | 2008-03-19T04:58:16Z | |
| dc.date.available | 2007-05-09 | en_US |
| dc.date.available | 2008-03-19T04:58:16Z | |
| dc.date.issued | 2004-09-01 | en_US |
| dc.identifier.citation | Bouckaert, 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.uri | https://hdl.handle.net/10289/85 | |
| dc.description.abstract | Various 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.mimetype | application/pdf | |
| dc.language.iso | en | |
| dc.publisher | Department of Computer Science | en_NZ |
| dc.title | Bayesian network classifiers in Weka | en_US |
| dc.type | Working Paper | en_US |
| pubs.elements-id | 52837 | |
| pubs.place-of-publication | Waikato University, Hamilton | en_NZ |
