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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification
verfasst von : Gherardo Varando, Concha Bielza, Pedro Larrañaga
Erschienen in: Probabilistic Graphical Models
Verlag: Springer International Publishing
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Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of
h
labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of
h
binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the
binary relevance method
and Bayesian network
chain classifiers
. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.