efficient approximation of the conditional relative entropy with applications to discriminative learning of bayesian network classifiers
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2013
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Abstract
We propose a minimum variance unbiased approximation to the conditional relative entropy of the distribution induced by the observed frequency estimates, for multi-classification tasks. Such approximation is an extension of a decomposable scoring criterion, named approximate conditional log-likelihood (aCLL), primarily used for discriminative learning of augmented Bayesian network classifiers. Our contribution is twofold: (i) it addresses multi-classification tasks and not only binary-classification ones; and (ii) it covers broader stochastic assumptions than uniform distribution over the parameters. Specifically, we considered a Dirichlet distribution over the parameters, which was experimentally shown to be a very good approximation to CLL. In addition, for Bayesian network classifiers, a closed-form equation is found for the parameters that maximize the scoring criterion.
| Reference Key |
mateus2013entropyefficient
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|---|---|
| Authors | ;Paulo Mateus;Pedro Adão;Alexandra M. Carvalho |
| Journal | European journal of medicinal chemistry |
| Year | 2013 |
| DOI |
10.3390/e15072716
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| URL | |
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