论文标题
贝叶斯网络分类器的可区分棕褐色结构学习
Differentiable TAN Structure Learning for Bayesian Network Classifiers
论文作者
论文摘要
学习贝叶斯网络的结构是一个困难的组合优化问题。在本文中，我们考虑了解具有离散输入特征的贝叶斯网络分类器的树凸起的幼稚贝叶斯（TAN）结构。所提出的方法没有在可能的图结构的空间上进行组合优化，而是学习了图形结构的分布。训练后，我们选择了此分布的最可能结构。这允许使用基于梯度的优化对贝叶斯网络参数进行联合培训及其棕褐色结构。所提出的方法对特定的损失不可知，只要求它是可区分的。我们使用基于歧视性概率边缘的杂种生成歧视损失进行广泛的实验。我们的方法始终优于随机棕褐色结构和chow-liu tan结构。
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.
