用线性回归模拟层次狄利克雷过程，提升贝叶斯网络分类器的参数估计速度与性能。
Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing
- 用对数线性模型近似层次狄利克雷过程的参数平滑机制。
- 在类别数据上超越HDP平滑，速度提升数量级且性能媲美随机森林。
- 适合需要快速、可解释分类模型的研究者使用。
贝叶斯网络分类器（BNCs）具备易解释、高可扩展性和可调复杂度等优点，但传统学习方法性能常落后于随机森林等主流分类器。近期基于层次狄利克雷过程（HDP）的参数平滑技术使BNC在类别数据上达到与随机森林相当的性能，但该方法灵活性差，采样过程复杂。本文提出一种新参数估计方法，通过对数线性回归近似HDP行为。作为线性模型，该方法灵活且易于解释，可利用丰富的线性模型研究资源。实验表明，该方法不仅显著快于HDP平滑，还能在类别数据上保持与随机森林相当的竞争力。
原文摘要 · Abstract (English)
Bayesian network classifiers (BNCs) possess a number of properties desirable for a modern classifier: They are easily interpretable, highly scalable, and offer adaptable complexity. However, traditional methods for learning BNCs have historically underperformed when compared to leading classification methods such as random forests. Recent parameter smoothing techniques using hierarchical Dirichlet processes (HDPs) have enabled BNCs to achieve performance competitive with random forests on categorical data, but these techniques are relatively inflexible, and require a complicated, specialized sampling process. In this paper, we introduce a novel method for parameter estimation that uses a log-linear regression to approximate the behaviour of HDPs. As a linear model, our method is remarkably flexible and simple to interpret, and can leverage the vast literature on learning linear models. Our experiments show that our method can outperform HDP smoothing while being orders of magnitude faster, remaining competitive with random forests on categorical data.
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