论文标题
FedHC贝叶斯网络学习算法
The FEDHC Bayesian network learning algorithm
论文作者
论文摘要
该论文提出了一种新的混合贝叶斯网络学习算法，该学习算法被称为“早期降山攀岩（FedHC）”，该算法设计为连续或分类变量。此外，本文表明，MMHC在统计软件\ textit {r}中的唯一实现非常昂贵，并且提供了新的实施。此外，提出了针对其他BN Learning算法可以采用的FedHC的稳健版本的持续数据的情况。通过蒙特卡洛模拟对FEDHC进行了测试，该模拟明显地表明其在计算上有效，并产生了与MMHC和PCHC相似或准确性的贝叶斯网络。最后，使用统计软件\ textit {r}证明了FedHC，PCHC和MMHC算法在实际数据中的应用。
The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, that can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show it is computationally efficient, and produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software \textit{R}.
