Alexandra M. Carvalho,

Inesc-ID

Abstract:

The aim of this work is to benchmark scoring functions used by
Bayesian network learning algorithms in the context of classification.
We considered both information-theoretic scores, such as LL, AIC,
BIC/MDL, NML and MIT, and Bayesian scores, such as K2, BD, BDe and
BDeu. We tested the scores in a classification task by learning the
optimal TAN classifier with benchmark datasets. We conclude that, in
general, information-theoretic scores perform better than Bayesian
scores.

 

Date: 2009-Apr-23     Time: 16:00:00     Room: 04


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