Differentiable TAN Structure Learning for Bayesian Network Classifiers

• URL: http://arxiv.org/abs/2008.09566v1
• Date: Fri, 21 Aug 2020 16:22:47 GMT
• Title: Differentiable TAN Structure Learning for Bayesian Network Classifiers
• Authors: Wolfgang Roth and Franz Pernkopf
• Abstract summary: We consider learning of tree-augmented naive Bayes (TAN) structures for Bayesian network classifiers with discrete input features. Instead of performing a optimization over the space of possible graph structures, the proposed method learns a distribution over graph structures. Our method consistently outperforms random TAN structures and Chow-Liu TAN structures.
• Score: 19.30562170076368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning the structure of Bayesian networks is a difficult combinatorial optimization problem. In this paper, we consider learning of tree-augmented naive Bayes (TAN) structures for Bayesian network classifiers with discrete input features. Instead of performing a combinatorial optimization over the space of possible graph structures, the proposed method learns a distribution over graph structures. After training, we select the most probable structure of this distribution. This allows for a joint training of the Bayesian network parameters along with its TAN structure using gradient-based optimization. The proposed method is agnostic to the specific loss and only requires that it is differentiable. We perform extensive experiments using a hybrid generative-discriminative loss based on the discriminative probabilistic margin. Our method consistently outperforms random TAN structures and Chow-Liu TAN structures.

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