论文标题
Bigbravebn：具有大量节点的贝叶斯网络的结构学习算法
BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes
论文作者
论文摘要
学习一个贝叶斯网络是一个NP硬质问题，并且随着节点的数量增加，学习贝叶斯网络结构的经典算法变得效率低下。近年来，开发了一些用于学习大量节点的贝叶斯网络（超过50个）的贝叶斯网络的方法和算法。但是这些解决方案的缺点，例如，它们仅操作一种类型的数据（离散或连续），或者已经创建了算法来满足数据的特定性质（医学，社交等）。本文介绍了一种用于学习大量节点（超过100个）的大型贝叶斯网络的Bigbravebn算法。该算法利用了勇敢的系数，该系数测量了几组实例的相互发生。为了形成这些组，我们根据共同信息（MI）度量使用最近的邻居方法。在本文的实验部分中，我们将BigBraveBN与其他现有解决方案的性能进行比较，既离散又连续的数据集。实验部分还代表了对实际数据的测试。上述实验结果表明，Bigbravebn算法在贝叶斯网络的结构学习中的效率。
Learning a Bayesian network is an NP-hard problem and with an increase in the number of nodes, classical algorithms for learning the structure of Bayesian networks become inefficient. In recent years, some methods and algorithms for learning Bayesian networks with a high number of nodes (more than 50) were developed. But these solutions have their disadvantages, for instance, they only operate one type of data (discrete or continuous) or their algorithm has been created to meet a specific nature of data (medical, social, etc.). The article presents a BigBraveBN algorithm for learning large Bayesian Networks with a high number of nodes (over 100). The algorithm utilizes the Brave coefficient that measures the mutual occurrence of instances in several groups. To form these groups, we use the method of nearest neighbours based on the Mutual information (MI) measure. In the experimental part of the article, we compare the performance of BigBraveBN to other existing solutions on multiple data sets both discrete and continuous. The experimental part also represents tests on real data. The aforementioned experimental results demonstrate the efficiency of the BigBraveBN algorithm in structure learning of Bayesian Networks.
