7,214 research outputs found
Decision functions for chain classifiers based on Bayesian networks for multi-label classification
Multi-label classification problems require each instance to be assigned a subset of a
defined set of labels. This problem is equivalent to finding a multi-valued decision function
that predicts a vector of binary classes. In this paper we study the decision boundaries of
two widely used approaches for building multi-label classifiers, when Bayesian networkaugmented
naive Bayes classifiers are used as base models: Binary relevance method
and chain classifiers. In particular extending previous single-label results to multi-label
chain classifiers, we find polynomial expressions for the multi-valued decision functions
associated with these methods. We prove upper boundings on the expressive power of
both methods and we prove that chain classifiers provide a more expressive model than
the binary relevance method
Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification
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
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