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Discriminative versus generative parameter and structure learning of Bayesian Network Classifiers (2005)
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| Venue: | In Intl. Conf. on Machine Learning |
| Citations: | 30 - 2 self |
BibTeX
@INPROCEEDINGS{Pernkopf05discriminativeversus,
author = {Franz Pernkopf and Jeff Bilmes},
title = {Discriminative versus generative parameter and structure learning of Bayesian Network Classifiers},
booktitle = {In Intl. Conf. on Machine Learning},
year = {2005},
pages = {657--664}
}
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Abstract
In this paper, we compare both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian network classifiers. We use either maximum likelihood (ML) or conditional maximum likelihood (CL) to optimize network parameters. For structure learning, we use either conditional mutual information (CMI), the explaining away residual (EAR), or the classification rate (CR) as objective functions. Experiments with the naive Bayes classifier (NB), the tree augmented naive Bayes classifier (TAN), and the Bayesian multinet have been performed on 25 data sets from the UCI repository (Merz et al., 1997) and from (Kohavi & John, 1997). Our empirical study suggests that discriminative structures learnt using CR produces the most accurate classifiers on almost half the data sets. This approach is feasible, however, only for rather small problems since it is computationally expensive. Discriminative parameter learning produces on average a better classifier than ML parameter learning. 1.
Keyphrases
bayesian network classifier structure learning discriminative versus generative parameter data set naive bayes classifier discriminative structure accurate classifier generative parameter conditional maximum likelihood conditional mutual information bayesian multinet objective function kohavi john ml parameter network parameter small problem empirical study maximum likelihood uci repository classification rate discriminative parameter
