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      • 2004 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2004 Working Papers
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      Bayesian network classifiers in Weka

      Bouckaert, Remco R.
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      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.
      Permanent Research Commons link: https://hdl.handle.net/10289/85
      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.
      Date
      2004-09-01
      Type
      Working Paper
      Publisher
      Department of Computer Science
      Collections
      • 2004 Working Papers [14]
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